Systems and methods of autonomously controlling vehicle states

ABSTRACT

Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

FIELD

The present disclosure is generally directed to vehicle systems, in particular, toward autonomously controlling a vehicle.

BACKGROUND

In recent years, transportation methods have changed substantially. This change is due in part to a concern over the limited availability of natural resources, a proliferation in personal technology, and a societal shift to adopt more environmentally friendly transportation solutions. These considerations have encouraged the development of a number of new flexible-fuel vehicles, hybrid-electric vehicles, and electric vehicles.

While these vehicles appear to be new, they are generally implemented as a number of traditional subsystems that are merely tied to an alternative power source. In fact, the design and construction of the vehicles is limited to standard frame sizes, shapes, materials, and transportation concepts. Among other things, these limitations fail to take advantage of the benefits of new technology, power sources, and support infrastructure.

To perform properly, autonomous vehicles must take into consideration a near infinite number of variables and factors. Contemporary autonomous vehicles, however, are limited by capabilities of memory and processors. An autonomous vehicle navigating through traffic at high speeds is faced with an ever-increasing number of decisions to make despite any shortcomings of processing technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a vehicle in accordance with embodiments of the present disclosure;

FIG. 2 shows a plan view of the vehicle in accordance with at least some embodiments of the present disclosure;

FIG. 3A is a block diagram of an embodiment of a communication environment of the vehicle in accordance with embodiments of the present disclosure;

FIG. 3B is a block diagram of an embodiment of interior sensors within the vehicle in accordance with embodiments of the present disclosure;

FIG. 3C is a block diagram of an embodiment of a navigation system of the vehicle in accordance with embodiments of the present disclosure;

FIG. 4 shows an embodiment of the instrument panel of the vehicle according to one embodiment of the present disclosure;

FIG. 5 is a block diagram of an embodiment of a communications subsystem of the vehicle;

FIG. 6 is a block diagram of a computing environment associated with the embodiments presented herein;

FIG. 7 is a block diagram of a computing device associated with one or more components described herein;

FIG. 8A is an illustration of an environment in accordance with one or more of the embodiments presented herein;

FIG. 8B is an illustration of a database in accordance with one or more of the embodiments presented herein;

FIG. 9A is an illustration of a phase plane trajectory graph in accordance with one or more of the embodiments presented herein;

FIG. 9B is an illustration of a phase plane trajectory graph in accordance with one or more of the embodiments presented herein;

FIG. 9C is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9D is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9E is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9F is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9G is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9H is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9I is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9J is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9K is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 9L is an illustration of a phase portrait in accordance with one or more of the embodiments presented herein;

FIG. 10A is an illustration of a contour plot in accordance with one or more of the embodiments presented herein;

FIG. 10B is an illustration of a contour plot in accordance with one or more of the embodiments presented herein;

FIG. 11 is a flow chart of an embodiment of a method of a control system in accordance with one or more of the embodiments presented herein.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connection with a vehicle, and in some embodiments, an electric vehicle, rechargeable electric vehicle, and/or hybrid-electric vehicle and associated systems.

FIG. 1 shows a perspective view of a vehicle 100 in accordance with embodiments of the present disclosure. The electric vehicle 100 comprises a vehicle front 110, vehicle aft or rear 120, vehicle roof 130, at least one vehicle side 160, a vehicle undercarriage 140, and a vehicle interior 150. In any event, the vehicle 100 may include a frame 104 and one or more body panels 108 mounted or affixed thereto. The vehicle 100 may include one or more interior components (e.g., components inside an interior space 150, or user space, of a vehicle 100, etc.), exterior components (e.g., components outside of the interior space 150, or user space, of a vehicle 100, etc.), drive systems, controls systems, structural components, etc.

Although shown in the form of a car, it should be appreciated that the vehicle 100 described herein may include any conveyance or model of a conveyance, where the conveyance was designed for the purpose of moving one or more tangible objects, such as people, animals, cargo, and the like. The term vehicle does not require that a conveyance moves or is capable of movement. Typical vehicles may include but are in no way limited to cars, trucks, motorcycles, busses, automobiles, trains, railed conveyances, boats, ships, marine conveyances, submarine conveyances, airplanes, space craft, flying machines, human-powered conveyances, and the like.

In some embodiments, the vehicle 100 may include a number of sensors, devices, and/or systems that are capable of assisting in driving operations, e.g., autonomous or semi-autonomous control. Examples of the various sensors and systems may include, but are in no way limited to, one or more of cameras (e.g., independent, stereo, combined image, etc.), infrared (IR) sensors, radio frequency (RF) sensors, ultrasonic sensors (e.g., transducers, transceivers, etc.), RADAR sensors (e.g., object-detection sensors and/or systems), LIDAR (Light Imaging, Detection, And Ranging) systems, odometry sensors and/or devices (e.g., encoders, etc.), orientation sensors (e.g., accelerometers, gyroscopes, magnetometer, etc.), navigation sensors and systems (e.g., GPS, etc.), and other ranging, imaging, and/or object-detecting sensors. The sensors may be disposed in an interior space 150 of the vehicle 100 and/or on an outside of the vehicle 100. In some embodiments, the sensors and systems may be disposed in one or more portions of a vehicle 100 (e.g., the frame 104, a body panel, a compartment, etc.).

The vehicle sensors and systems may be selected and/or configured to suit a level of operation associated with the vehicle 100. Among other things, the number of sensors used in a system may be altered to increase or decrease information available to a longitudinal planning system (e.g., affecting control capabilities of the vehicle 100). Additionally, or alternatively, the sensors and systems may be part of one or more advanced driver assistance systems (ADAS) associated with a vehicle 100. In any event, the sensors and systems may be used to provide driving assistance at any level of operation (e.g., from fully-manual to fully-autonomous operations, etc.) as described herein.

The various levels of vehicle control and/or operation can be described as corresponding to a level of autonomy associated with a vehicle 100 for vehicle driving operations. For instance, at Level 0, or fully-manual driving operations, a driver (e.g., a human driver) may be responsible for all the driving control operations (e.g., steering, accelerating, braking, etc.) associated with the vehicle. Level 0 may be referred to as a “No Automation” level. At Level 1, the vehicle may be responsible for a limited number of the driving operations associated with the vehicle, while the driver is still responsible for most driving control operations. An example of a Level 1 vehicle may include a vehicle in which the throttle control and/or braking operations may be controlled by the vehicle (e.g., cruise control operations, etc.). Level 1 may be referred to as a “Driver Assistance” level. At Level 2, the vehicle may collect information (e.g., via one or more driving assistance systems, sensors, etc.) about an environment of the vehicle (e.g., surrounding area, roadway, traffic, ambient conditions, etc.) and use the collected information to control driving operations (e.g., steering, accelerating, braking, etc.) associated with the vehicle. In a Level 2 autonomous vehicle, the driver may be required to perform other aspects of driving operations not controlled by the vehicle. Level 2 may be referred to as a “Partial Automation” level. It should be appreciated that Levels 0-2 all involve the driver monitoring the driving operations of the vehicle.

At Level 3, the driver may be separated from controlling all the driving operations of the vehicle except when the vehicle makes a request for the operator to act or intervene in controlling one or more driving operations. In other words, the driver may be separated from controlling the vehicle unless the driver is required to take over for the vehicle. Level 3 may be referred to as a “Conditional Automation” level. At Level 4, the driver may be separated from controlling all the driving operations of the vehicle and the vehicle may control driving operations even when a user fails to respond to a request to intervene. Level 4 may be referred to as a “High Automation” level. At Level 5, the vehicle can control all the driving operations associated with the vehicle in all driving modes. The vehicle in Level 5 may continually monitor traffic, vehicular, roadway, and/or environmental conditions while driving the vehicle. In Level 5, there is no human driver interaction required in any driving mode. Accordingly, Level 5 may be referred to as a “Full Automation” level. It should be appreciated that in Levels 3-5 the vehicle, and/or one or more automated driving systems associated with the vehicle, monitors the driving operations of the vehicle and the driving environment.

As shown in FIG. 1, the vehicle 100 may, for example, include at least one of a ranging and imaging system 112 (e.g., LIDAR, etc.), an imaging sensor 116A, 116F (e.g., camera, IR, etc.), a radio object-detection and ranging system sensors 116B (e.g., RADAR, RF, etc.), ultrasonic sensors 116C, and/or other object-detection sensors 116D, 116E. In some embodiments, the LIDAR system 112 and/or sensors may be mounted on a roof 130 of the vehicle 100. In one embodiment, the RADAR sensors 116B may be disposed at least at a front 110, aft 120, or side 160 of the vehicle 100. Among other things, the RADAR sensors may be used to monitor and/or detect a position of other vehicles, pedestrians, and/or other objects near, or proximal to, the vehicle 100. While shown associated with one or more areas of a vehicle 100, it should be appreciated that any of the sensors and systems 116A-K, 112 illustrated in FIGS. 1 and 2 may be disposed in, on, and/or about the vehicle 100 in any position, area, and/or zone of the vehicle 100.

Referring now to FIG. 2, a plan view of a vehicle 100 will be described in accordance with embodiments of the present disclosure. In particular, FIG. 2 shows a vehicle sensing environment 200 at least partially defined by the sensors and systems 116A-K, 112 disposed in, on, and/or about the vehicle 100. Each sensor 116A-K may include an operational detection range R and operational detection angle. The operational detection range R may define the effective detection limit, or distance, of the sensor 116A-K. In some cases, this effective detection limit may be defined as a distance from a portion of the sensor 116A-K (e.g., a lens, sensing surface, etc.) to a point in space offset from the sensor 116A-K. The effective detection limit may define a distance, beyond which, the sensing capabilities of the sensor 116A-K deteriorate, fail to work, or are unreliable. In some embodiments, the effective detection limit may define a distance, within which, the sensing capabilities of the sensor 116A-K are able to provide accurate and/or reliable detection information. The operational detection angle may define at least one angle of a span, or between horizontal and/or vertical limits, of a sensor 116A-K. As can be appreciated, the operational detection limit and the operational detection angle of a sensor 116A-K together may define the effective detection zone 216A-D (e.g., the effective detection area, and/or volume, etc.) of a sensor 116A-K.

In some embodiments, the vehicle 100 may include a ranging and imaging system 112 such as LIDAR, or the like. The ranging and imaging system 112 may be configured to detect visual information in an environment surrounding the vehicle 100. The visual information detected in the environment surrounding the ranging and imaging system 112 may be processed (e.g., via one or more sensor and/or system processors, etc.) to generate a complete 360-degree view of an environment 200 around the vehicle. The ranging and imaging system 112 may be configured to generate changing 360-degree views of the environment 200 in real-time, for instance, as the vehicle 100 drives. In some cases, the ranging and imaging system 112 may have an effective detection limit 204 that is some distance from the center of the vehicle 100 outward over 360 degrees. The effective detection limit 204 of the ranging and imaging system 112 defines a view zone 208 (e.g., an area and/or volume, etc.) surrounding the vehicle 100. Any object falling outside of the view zone 208 is in the undetected zone 212 and would not be detected by the ranging and imaging system 112 of the vehicle 100.

Sensor data and information may be collected by one or more sensors or systems 116A-K, 112 of the vehicle 100 monitoring the vehicle sensing environment 200. This information may be processed (e.g., via a processor, computer-vision system, etc.) to determine targets (e.g., objects, signs, people, markings, roadways, conditions, etc.) inside one or more detection zones 208, 216A-D associated with the vehicle sensing environment 200. In some cases, information from multiple sensors 116A-K may be processed to form composite sensor detection information. For example, a first sensor 116A and a second sensor 116F may correspond to a first camera 116A and a second camera 116F aimed in a forward traveling direction of the vehicle 100. In this example, images collected by the cameras 116A, 116F may be combined to form stereo image information. This composite information may increase the capabilities of a single sensor in the one or more sensors 116A-K by, for example, adding the ability to determine depth associated with targets in the one or more detection zones 208, 216A-D. Similar image data may be collected by rear view cameras (e.g., sensors 116G, 116H) aimed in a rearward traveling direction vehicle 100.

In some embodiments, multiple sensors 116A-K may be effectively joined to increase a sensing zone and provide increased sensing coverage. For instance, multiple RADAR sensors 116B disposed on the front 110 of the vehicle may be joined to provide a zone 216B of coverage that spans across an entirety of the front 110 of the vehicle. In some cases, the multiple RADAR sensors 116B may cover a detection zone 216B that includes one or more other sensor detection zones 216A. These overlapping detection zones may provide redundant sensing, enhanced sensing, and/or provide greater detail in sensing within a particular portion (e.g., zone 216A) of a larger zone (e.g., zone 216B). Additionally, or alternatively, the sensors 116A-K of the vehicle 100 may be arranged to create a complete coverage, via one or more sensing zones 208, 216A-D around the vehicle 100. In some areas, the sensing zones 216C of two or more sensors 116D, 116E may intersect at an overlap zone 220. In some areas, the angle and/or detection limit of two or more sensing zones 216C, 216D (e.g., of two or more sensors 116E, 116J, 116K) may meet at a virtual intersection point 224.

The vehicle 100 may include a number of sensors 116E, 116G, 116H, 116J, 116K disposed proximal to the rear 120 of the vehicle 100. These sensors can include, but are in no way limited to, an imaging sensor, camera, IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonic sensors, and/or other object-detection sensors. Among other things, these sensors 116E, 116G, 116H, 116J, 116K may detect targets near or approaching the rear of the vehicle 100. For example, another vehicle approaching the rear 120 of the vehicle 100 may be detected by one or more of the ranging and imaging system (e.g., LIDAR) 112, rear-view cameras 116G, 116H, and/or rear facing RADAR sensors 116J, 116K. As described above, the images from the rear-view cameras 116G, 116H may be processed to generate a stereo view (e.g., providing depth associated with an object or environment, etc.) for targets visible to both cameras 116G, 116H. As another example, the vehicle 100 may be driving and one or more of the ranging and imaging system 112, front-facing cameras 116A, 116F, front-facing RADAR sensors 116B, and/or ultrasonic sensors 116C may detect targets in front of the vehicle 100. This approach may provide critical sensor information to a longitudinal planning system in at least one of the autonomous driving levels described above. For instance, when the vehicle 100 is driving autonomously (e.g., Level 3, Level 4, or Level 5) and detects other vehicles stopped in a travel path, the sensor detection information may be sent to the longitudinal planning system of the vehicle 100 to control a driving operation (e.g., braking, decelerating, etc.) associated with the vehicle 100 (in this example, slowing the vehicle 100 as to avoid colliding with the stopped other vehicles). As yet another example, the vehicle 100 may be operating and one or more of the ranging and imaging system 112, and/or the side-facing sensors 116D, 116E (e.g., RADAR, ultrasonic, camera, combinations thereof, and/or other type of sensor), may detect targets at a side of the vehicle 100. It should be appreciated that the sensors 116A-K may detect a target that is both at a side 160 and a front 110 of the vehicle 100 (e.g., disposed at a diagonal angle to a centerline of the vehicle 100 running from the front 110 of the vehicle 100 to the rear 120 of the vehicle). Additionally, or alternatively, the sensors 116A-K may detect a target that is both, or simultaneously, at a side 160 and a rear 120 of the vehicle 100 (e.g., disposed at a diagonal angle to the centerline of the vehicle 100).

FIGS. 3A-3C are block diagrams of an embodiment of a communication environment 300 of the vehicle 100 in accordance with embodiments of the present disclosure. The communication system 300 may include one or more vehicle driving vehicle sensors and systems 304, sensor processors 340, sensor data memory 344, longitudinal planning system 348, communications subsystem 350, control data 364, computing devices 368, display devices 372, and other components 374 that may be associated with a vehicle 100. These associated components may be electrically and/or communicatively coupled to one another via at least one bus 360. In some embodiments, the one or more associated components may send and/or receive signals across a communication network 352 to at least one of a navigation source 356A, a control source 356B, or some other entity 356N.

In accordance with at least some embodiments of the present disclosure, the communication network 352 may comprise any type of known communication medium or collection of communication media and may use any type of protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and the like, to transport messages between endpoints. The communication network 352 may include wired and/or wireless communication technologies. The Internet is an example of the communication network 352 that constitutes an Internet Protocol (IP) network consisting of many computers, computing networks, and other communication devices located all over the world, which are connected through many telephone systems and other means. Other examples of the communication network 352 include, without limitation, a standard Plain Old Telephone System (POTS), an Integrated Services Digital Network (ISDN), the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), such as an Ethernet network, a Token-Ring network and/or the like, a Wide Area Network (WAN), a virtual network, including without limitation a virtual private network (“VPN”); the Internet, an intranet, an extranet, a cellular network, an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art, and/or any other wireless protocol), and any other type of packet-switched or circuit-switched network known in the art and/or any combination of these and/or other networks. In addition, it can be appreciated that the communication network 352 need not be limited to any one network type, and instead may be comprised of a number of different networks and/or network types. The communication network 352 may comprise a number of different communication media such as coaxial cable, copper cable/wire, fiber-optic cable, antennas for transmitting/receiving wireless messages, and combinations thereof.

The driving vehicle sensors and systems 304 may include at least one navigation 308 (e.g., global positioning system (GPS), etc.), orientation 312, odometry 316, LIDAR 320, RADAR 324, ultrasonic 328, camera 332, infrared (IR) 336, and/or other sensor or system 338. These driving vehicle sensors and systems 304 may be similar, if not identical, to the sensors and systems 116A-K, 112 described in conjunction with FIGS. 1 and 2.

The navigation sensor 308 may include one or more sensors having receivers and antennas that are configured to utilize a satellite-based navigation system including a network of navigation satellites capable of providing geolocation and time information to at least one component of the vehicle 100. Examples of the navigation sensor 308 as described herein may include, but are not limited to, at least one of Garmin® GLO™ family of GPS and GLONASS combination sensors, Garmin® GPS 15x™ family of sensors, Garmin® GPS 16x™ family of sensors with high-sensitivity receiver and antenna, Garmin® GPS 18x OEM family of high-sensitivity GPS sensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz series of GPS sensors, other industry-equivalent navigation sensors and/or systems, and may perform navigational and/or geolocation functions using any known or future-developed standard and/or architecture.

The orientation sensor 312 may include one or more sensors configured to determine an orientation of the vehicle 100 relative to at least one reference point. In some embodiments, the orientation sensor 312 may include at least one pressure transducer, stress/strain gauge, accelerometer, gyroscope, and/or geomagnetic sensor. Examples of the navigation sensor 308 as described herein may include, but are not limited to, at least one of Bosch Sensortec BMX 160 series low-power absolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors, Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI160 6-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors (accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+ microcontroller, Bosch Sensortec BMP280 absolute barometric pressure sensors, Infineon TLV493D-A1B6 3D magnetic sensors, Infineon TLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magnetic sensors, Murata Electronics SCC2000 series combined gyro sensor and accelerometer, Murata Electronics SCC1300 series combined gyro sensor and accelerometer, other industry-equivalent orientation sensors and/or systems, which may perform orientation detection and/or determination functions using any known or future-developed standard and/or architecture.

The odometry sensor and/or system 316 may include one or more components that is configured to determine a change in position of the vehicle 100 over time. In some embodiments, the odometry system 316 may utilize data from one or more other sensors and/or systems 304 in determining a position (e.g., distance, location, etc.) of the vehicle 100 relative to a previously measured position for the vehicle 100. Additionally, or alternatively, the odometry sensors 316 may include one or more encoders, Hall speed sensors, and/or other measurement sensors/devices configured to measure a wheel speed, rotation, and/or number of revolutions made over time. Examples of the odometry sensor/system 316 as described herein may include, but are not limited to, at least one of Infineon TLE4924/26/27/28C high-performance speed sensors, Infineon TL4941plusC(B) single chip differential Hall wheel-speed sensors, Infineon TL5041plusC Giant Magnetoresistance (GMR) effect sensors, Infineon TL family of magnetic sensors, EPC Model 25SP Accu-CoderPro™ incremental shaft encoders, EPC Model 30M compact incremental encoders with advanced magnetic sensing and signal processing technology, EPC Model 925 absolute shaft encoders, EPC Model 958 absolute shaft encoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders, Dynapar™ F18 commutating optical encoder, Dynapar™ HS35R family of phased array encoder sensors, other industry-equivalent odometry sensors and/or systems, and may perform change in position detection and/or determination functions using any known or future-developed standard and/or architecture.

The LIDAR sensor/system 320 may include one or more components configured to measure distances to targets using laser illumination. In some embodiments, the LIDAR sensor/system 320 may provide 3D imaging data of an environment around the vehicle 100. The imaging data may be processed to generate a full 360-degree view of the environment around the vehicle 100. The LIDAR sensor/system 320 may include a laser light generator configured to generate a plurality of target illumination laser beams (e.g., laser light channels). In some embodiments, this plurality of laser beams may be aimed at, or directed to, a rotating reflective surface (e.g., a mirror) and guided outwardly from the LIDAR sensor/system 320 into a measurement environment. The rotating reflective surface may be configured to continually rotate 360 degrees about an axis, such that the plurality of laser beams is directed in a full 360-degree range around the vehicle 100. A photodiode receiver of the LIDAR sensor/system 320 may detect when light from the plurality of laser beams emitted into the measurement environment returns (e.g., reflected echo) to the LIDAR sensor/system 320. The LIDAR sensor/system 320 may calculate, based on a time associated with the emission of light to the detected return of light, a distance from the vehicle 100 to the illuminated target. In some embodiments, the LIDAR sensor/system 320 may generate over 2.0 million points per second and have an effective operational range of at least 100 meters. Examples of the LIDAR sensor/system 320 as described herein may include, but are not limited to, at least one of Velodyne® LiDAR™ HDL-64E 64-channel LIDAR sensors, Velodyne® LiDAR™ HDL-32E 32-channel LIDAR sensors, Velodyne® LiDAR™ PUCK™ VLP-16 16-channel LIDAR sensors, Leica Geosystems Pegasus:Two mobile sensor platform, Garmin® LIDAR-Lite v3 measurement sensor, Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor, LeddarTech® LeddarVU compact solid state fixed-beam LIDAR sensors, other industry-equivalent LIDAR sensors and/or systems, and may perform illuminated target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The RADAR sensors 324 may include one or more radio components that are configured to detect objects/targets in an environment of the vehicle 100. In some embodiments, the RADAR sensors 324 may determine a distance, position, and/or movement vector (e.g., angle, speed, etc.) associated with a target over time. The RADAR sensors 324 may include a transmitter configured to generate and emit electromagnetic waves (e.g., radio, microwaves, etc.) and a receiver configured to detect returned electromagnetic waves. In some embodiments, the RADAR sensors 324 may include at least one processor configured to interpret the returned electromagnetic waves and determine locational properties of targets. Examples of the RADAR sensors 324 as described herein may include, but are not limited to, at least one of Infineon RASIC™ RTN7735PL transmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP Vehicle RADAR sensors, Delphi L2C0051TR 77 GHz ESR Electronically Scanning Radar sensors, Fujitsu Ten Ltd. Automotive Compact 77 GHz 3D Electronic Scan Millimeter Wave Radar sensors, other industry-equivalent RADAR sensors and/or systems, and may perform radio target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The ultrasonic sensors 328 may include one or more components that are configured to detect objects/targets in an environment of the vehicle 100. In some embodiments, the ultrasonic sensors 328 may determine a distance, position, and/or movement vector (e.g., angle, speed, etc.) associated with a target over time. The ultrasonic sensors 328 may include an ultrasonic transmitter and receiver, or transceiver, configured to generate and emit ultrasound waves and interpret returned echoes of those waves. In some embodiments, the ultrasonic sensors 328 may include at least one processor configured to interpret the returned ultrasonic waves and determine locational properties of targets. Examples of the ultrasonic sensors 328 as described herein may include, but are not limited to, at least one of Texas Instruments TIDA-00151 automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450 ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonic proximity sensors, Murata Electronics MA40H 1S-R open-structure ultrasonic sensors, Murata Electronics MA40S4R/S open-structure ultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonic sensors, other industry-equivalent ultrasonic sensors and/or systems, and may perform ultrasonic target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The camera sensors 332 may include one or more components configured to detect image information associated with an environment of the vehicle 100. In some embodiments, the camera sensors 332 may include a lens, filter, image sensor, and/or a digital image processer. It is an aspect of the present disclosure that multiple camera sensors 332 may be used together to generate stereo images providing depth measurements. Examples of the camera sensors 332 as described herein may include, but are not limited to, at least one of ON Semiconductor® MT9V024 Global Shutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camera sensors, CMOSIS CMV50000 high-speed CMOS image sensors, other industry-equivalent camera sensors and/or systems, and may perform visual target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The infrared (IR) sensors 336 may include one or more components configured to detect image information associated with an environment of the vehicle 100. The IR sensors 336 may be configured to detect targets in low-light, dark, or poorly-lit environments. The IR sensors 336 may include an IR light emitting element (e.g., IR light emitting diode (LED), etc.) and an IR photodiode. In some embodiments, the IR photodiode may be configured to detect returned IR light at or about the same wavelength to that emitted by the IR light emitting element. In some embodiments, the IR sensors 336 may include at least one processor configured to interpret the returned IR light and determine locational properties of targets. The IR sensors 336 may be configured to detect and/or measure a temperature associated with a target (e.g., an object, pedestrian, other vehicle, etc.). Examples of IR sensors 336 as described herein may include, but are not limited to, at least one of Opto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LED sensors, Opto Diode SA/SHA727 steady state IR emitters and IR detectors, FUR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA and HD MWIR thermal sensors, FUR® VOx 640×480 pixel detector sensors, Delphi IR sensors, other industry-equivalent IR sensors and/or systems, and may perform IR visual target and/or obstacle detection in an environment around the vehicle 100 using any known or future-developed standard and/or architecture.

The vehicle 100 can also include one or more interior sensors 337. Interior sensors 337 can measure characteristics of the inside environment of the vehicle 100. The interior sensors 337 may be as described in conjunction with FIG. 3B.

A navigation system 302 can include any hardware and/or software used to navigate the vehicle either manually or autonomously. The navigation system 302 may be as described in conjunction with FIG. 3C.

In some embodiments, the driving vehicle sensors and systems 304 may include other sensors 338 and/or combinations of the sensors 306-337 described above. Additionally, or alternatively, one or more of the sensors 306-337 described above may include one or more processors configured to process and/or interpret signals detected by the one or more sensors 306-337. In some embodiments, the processing of at least some sensor information provided by the vehicle sensors and systems 304 may be processed by at least one sensor processor 340. Raw and/or processed sensor data may be stored in a sensor data memory 344 storage medium. In some embodiments, the sensor data memory 344 may store instructions used by the sensor processor 340 for processing sensor information provided by the sensors and systems 304. In any event, the sensor data memory 344 may be a disk drive, optical storage device, solid-state storage device such as a random-access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like.

The longitudinal planning system 348 may receive processed sensor information from the sensor processor 340 and determine to control an aspect of the vehicle 100. Controlling an aspect of the vehicle 100 may include presenting information via one or more display devices 372 associated with the vehicle, sending commands to one or more computing devices 368 associated with the vehicle, and/or controlling a driving operation of the vehicle. In some embodiments, the longitudinal planning system 348 may correspond to one or more computing systems that control driving operations of the vehicle 100 in accordance with the Levels of driving autonomy described above. In one embodiment, the longitudinal planning system 348 may operate a speed of the vehicle 100 by controlling an output signal to the accelerator and/or braking system of the vehicle. In this example, the longitudinal planning system 348 may receive sensor data describing an environment surrounding the vehicle 100 and, based on the sensor data received, determine to adjust the acceleration, power output, and/or braking of the vehicle 100. The longitudinal planning system 348 may additionally control steering and/or other driving functions of the vehicle 100.

The longitudinal planning system 348 may communicate, in real-time, with the driving sensors and systems 304 forming a feedback loop. In particular, upon receiving sensor information describing a condition of targets in the environment surrounding the vehicle 100, the longitudinal planning system 348 may autonomously make changes to a driving operation of the vehicle 100. The longitudinal planning system 348 may then receive subsequent sensor information describing any change to the condition of the targets detected in the environment as a result of the changes made to the driving operation. This continual cycle of observation (e.g., via the sensors, etc.) and action (e.g., selected control or non-control of vehicle operations, etc.) allows the vehicle 100 to operate autonomously in the environment.

In some embodiments, the one or more components of the vehicle 100 (e.g., the driving vehicle sensors 304, longitudinal planning system 348, display devices 372, etc.) may communicate across the communication network 352 to one or more entities 356A-N via a communications subsystem 350 of the vehicle 100. Embodiments of the communications subsystem 350 are described in greater detail in conjunction with FIG. 5. For instance, the navigation sensors 308 may receive global positioning, location, and/or navigational information from a navigation source 356A. In some embodiments, the navigation source 356A may be a global navigation satellite system (GNSS) similar, if not identical, to NAVSTAR GPS, GLONASS, EU Galileo, and/or the BeiDou Navigation Satellite System (BDS) to name a few.

In some embodiments, the longitudinal planning system 348 may receive control information from one or more control sources 356B. The control source 356 may provide vehicle control information including autonomous driving control commands, vehicle operation override control commands, and the like. The control source 356 may correspond to an autonomous longitudinal planning system, a traffic control system, an administrative control entity, and/or some other controlling server. It is an aspect of the present disclosure that the longitudinal planning system 348 and/or other components of the vehicle 100 may exchange communications with the control source 356 across the communication network 352 and via the communications subsystem 350.

Information associated with controlling driving operations of the vehicle 100 may be stored in a control data memory 364 storage medium. The control data memory 364 may store instructions used by the longitudinal planning system 348 for controlling driving operations of the vehicle 100, historical control information, autonomous driving control rules, and the like. In some embodiments, the control data memory 364 may be a disk drive, optical storage device, solid-state storage device such as a random-access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like.

In addition to the mechanical components described herein, the vehicle 100 may include a number of user interface devices. The user interface devices receive and translate human input into a mechanical movement or electrical signal or stimulus. The human input may be one or more of motion (e.g., body movement, body part movement, in two-dimensional or three-dimensional space, etc.), voice, touch, and/or physical interaction with the components of the vehicle 100. In some embodiments, the human input may be configured to control one or more functions of the vehicle 100 and/or systems of the vehicle 100 described herein. User interfaces may include, but are in no way limited to, at least one graphical user interface of a display device, steering wheel or mechanism, transmission lever or button (e.g., including park, neutral, reverse, and/or drive positions, etc.), throttle control pedal or mechanism, brake control pedal or mechanism, power control switch, communications equipment, etc.

FIG. 3B shows a block diagram of an embodiment of interior sensors 337 for a vehicle 100. The interior sensors 337 may be arranged into one or more groups, based at least partially on the function of the interior sensors 337. For example, the interior space of a vehicle 100 may include environmental sensors, user interface sensor(s), and/or safety sensors. Additionally, or alternatively, there may be sensors associated with various devices inside the vehicle (e.g., smart phones, tablets, mobile computers, wearables, etc.)

Environmental sensors may comprise sensors configured to collect data relating to the internal environment of a vehicle 100. Examples of environmental sensors may include one or more of but are not limited to: oxygen/air sensors 301, temperature sensors 303, humidity sensors 305, light/photo sensors 307, and more. The oxygen/air sensors 301 may be configured to detect a quality or characteristic of the air in the interior space 108 of the vehicle 100 (e.g., ratios and/or types of gasses comprising the air inside the vehicle 100, dangerous gas levels, safe gas levels, etc.). Temperature sensors 303 may be configured to detect temperature readings of one or more objects, users 216, and/or areas of a vehicle 100. Humidity sensors 305 may detect an amount of water vapor present in the air inside the vehicle 100. The light/photo sensors 307 can detect an amount of light present in the vehicle 100. Further, the light/photo sensors 307 may be configured to detect various levels of light intensity associated with light in the vehicle 100.

User interface sensors may comprise sensors configured to collect data relating to one or more users (e.g., a driver and/or passenger(s)) in a vehicle 100. As can be appreciated, the user interface sensors may include sensors that are configured to collect data from users 216 in one or more areas of the vehicle 100. Examples of user interface sensors may include one or more of, but are not limited to: infrared sensors 309, motion sensors 311, weight sensors 313, wireless network sensors 315, biometric sensors 317, camera (or image) sensors 319, audio sensors 321, and more.

Infrared sensors 309 may be used to measure IR light irradiating from at least one surface, user, or another object in the vehicle 100. Among other things, the infrared sensors 309 may be used to measure temperatures, form images (especially in low light conditions), identify users 216, and even detect motion in the vehicle 100.

The motion sensors 311 may detect motion and/or movement of objects inside the vehicle 100. Optionally, the motion sensors 311 may be used alone or in combination to detect movement. For example, a user may be operating a vehicle 100 (e.g., while driving, etc.) when a passenger in the rear of the vehicle 100 unbuckles a safety belt and proceeds to move about the vehicle 10. In this example, the movement of the passenger could be detected by the motion sensors 311. In response to detecting the movement and/or the direction associated with the movement, the passenger may be prevented from interfacing with and/or accessing at least some of the vehicle control features. As can be appreciated, the user may be alerted of the movement/motion such that the user can act to prevent the passenger from interfering with the vehicle controls. Optionally, the number of motion sensors in a vehicle may be increased to increase an accuracy associated with motion detected in the vehicle 100.

Weight sensors 313 may be employed to collect data relating to objects and/or users in various areas of the vehicle 100. In some cases, the weight sensors 313 may be included in the seats and/or floor of a vehicle 100. Optionally, the vehicle 100 may include a wireless network sensor 315. This sensor 315 may be configured to detect one or more wireless network(s) inside the vehicle 100. Examples of wireless networks may include, but are not limited to, wireless communications utilizing Bluetooth®, Wi-Fi™, ZigBee, IEEE 802.11, and other wireless technology standards. For example, a mobile hotspot may be detected inside the vehicle 100 via the wireless network sensor 315. In this case, the vehicle 100 may determine to utilize and/or share the mobile hotspot detected via/with one or more other devices associated with the vehicle 100.

Biometric sensors 317 may be employed to identify and/or record characteristics associated with a user. It is anticipated that biometric sensors 317 can include at least one of image sensors, IR sensors, fingerprint readers, weight sensors, load cells, force transducers, heart rate monitors, blood pressure monitors, and the like as provided herein.

The camera sensors 319 may record still images, video, and/or combinations thereof. Camera sensors 319 may be used alone or in combination to identify objects, users, and/or other features, inside the vehicle 100. Two or more camera sensors 319 may be used in combination to form, among other things, stereo and/or three-dimensional (3D) images. The stereo images can be recorded and/or used to determine depth associated with objects and/or users in a vehicle 100. Further, the camera sensors 319 used in combination may determine the complex geometry associated with identifying characteristics of a user. For example, the camera sensors 319 may be used to determine dimensions between various features of a user's face (e.g., the depth/distance from a user's nose to a user's cheeks, a linear distance between the center of a user's eyes, and more). These dimensions may be used to verify, record, and even modify characteristics that serve to identify a user. The camera sensors 319 may also be used to determine movement associated with objects and/or users within the vehicle 100. It should be appreciated that the number of image sensors used in a vehicle 100 may be increased to provide greater dimensional accuracy and/or views of a detected image in the vehicle 100.

The audio sensors 321 may be configured to receive audio input from a user of the vehicle 100. The audio input from a user may correspond to voice commands, conversations detected in the vehicle 100, phone calls made in the vehicle 100, and/or other audible expressions made in the vehicle 100. Audio sensors 321 may include, but are not limited to, microphones and other types of acoustic-to-electric transducers or sensors. Optionally, the interior audio sensors 321 may be configured to receive and convert sound waves into an equivalent analog or digital signal. The interior audio sensors 321 may serve to determine one or more locations associated with various sounds in the vehicle 100. The location of the sounds may be determined based on a comparison of volume levels, intensity, and the like, between sounds detected by two or more interior audio sensors 321. For instance, a first audio sensors 321 may be located in a first area of the vehicle 100 and a second audio sensors 321 may be located in a second area of the vehicle 100. If a sound is detected at a first volume level by the first audio sensors 321A and a second, higher, volume level by the second audio sensors 321 in the second area of the vehicle 100, the sound may be determined to be closer to the second area of the vehicle 100. As can be appreciated, the number of sound receivers used in a vehicle 100 may be increased (e.g., more than two, etc.) to increase measurement accuracy surrounding sound detection and location, or source, of the sound (e.g., via triangulation, etc.).

The safety sensors may comprise sensors configured to collect data relating to the safety of a user and/or one or more components of a vehicle 100. Examples of safety sensors may include one or more of, but are not limited to: force sensors 325, mechanical motion sensors 327, orientation sensors 329, restraint sensors 331, and more.

The force sensors 325 may include one or more sensors inside the vehicle 100 configured to detect a force observed in the vehicle 100. One example of a force sensor 325 may include a force transducer that converts measured forces (e.g., force, weight, pressure, etc.) into output signals. Mechanical motion sensors 327 may correspond to encoders, accelerometers, damped masses, and the like. Optionally, the mechanical motion sensors 327 may be adapted to measure the force of gravity (i.e., G-force) as observed inside the vehicle 100. Measuring the G-force observed inside a vehicle 100 can provide valuable information related to a vehicle's acceleration, deceleration, collisions, and/or forces that may have been suffered by one or more users in the vehicle 100. Orientation sensors 329 can include accelerometers, gyroscopes, magnetic sensors, and the like that are configured to detect an orientation associated with the vehicle 100.

The restraint sensors 331 may correspond to sensors associated with one or more restraint devices and/or systems in a vehicle 100. Seatbelts and airbags are examples of restraint devices and/or systems. As can be appreciated, the restraint devices and/or systems may be associated with one or more sensors that are configured to detect a state of the device/system. The state may include extension, engagement, retraction, disengagement, deployment, and/or other electrical or mechanical conditions associated with the device/system.

The associated device sensors 323 can include any sensors that are associated with a device in the vehicle 100. As previously stated, typical devices may include smart phones, tablets, laptops, mobile computers, and the like. It is anticipated that the various sensors associated with these devices can be employed by the longitudinal planning system 348. For example, a typical smart phone can include, an image sensor, an IR sensor, audio sensor, gyroscope, accelerometer, wireless network sensor, fingerprint reader, and more. It is an aspect of the present disclosure that one or more of these associated device sensors 323 may be used by one or more subsystems of the vehicle 100.

FIG. 3C illustrates a GPS/Navigation subsystem(s) 302. The navigation subsystem(s) 302 can be any present or future-built navigation system that may use location data, for example, from the Global Positioning System (GPS), to provide navigation information or control the vehicle 100. The navigation subsystem(s) 302 can include several components, such as, one or more of, but not limited to: a GPS Antenna/receiver 331, a location module 333, a maps database 335, etc. Generally, the several components or modules 331-335 may be hardware, software, firmware, computer readable media, or combinations thereof.

A GPS Antenna/receiver 331 can be any antenna, GPS puck, and/or receiver capable of receiving signals from a GPS satellite or other navigation system. The signals may be demodulated, converted, interpreted, etc. by the GPS Antenna/receiver 331 and provided to the location module 333. Thus, the GPS Antenna/receiver 331 may convert the time signals from the GPS system and provide a location (e.g., coordinates on a map) to the location module 333. Alternatively, the location module 333 can interpret the time signals into coordinates or other location information.

The location module 333 can be the controller of the satellite navigation system designed for use in the vehicle 100. The location module 333 can acquire position data, as from the GPS Antenna/receiver 331, to locate the user or vehicle 100 on a road in the unit's map database 335. Using the road database 335, the location module 333 can give directions to other locations along roads also in the database 335. When a GPS signal is not available, the location module 333 may apply dead reckoning to estimate distance data from sensors 304 including one or more of, but not limited to, a speed sensor attached to the drive train of the vehicle 100, a gyroscope, an accelerometer, etc. Additionally, or alternatively, the location module 333 may use known locations of Wi-Fi hotspots, cell tower data, etc. to determine the position of the vehicle 100, such as by using time difference of arrival (TDOA) and/or frequency difference of arrival (FDOA) techniques.

The maps database 335 can include any hardware and/or software to store information about maps, geographical information system (GIS) information, location information, etc. The maps database 335 can include any data definition or other structure to store the information. Generally, the maps database 335 can include a road database that may include one or more vector maps of areas of interest. Street names, street numbers, house numbers, and other information can be encoded as geographic coordinates so that the user can find some desired destination by street address. Points of interest (waypoints) can also be stored with their geographic coordinates. For example, a point of interest may include speed cameras, fuel stations, public parking, and “parked here” (or “you parked here”) information. The maps database 335 may also include road or street characteristics, for example, speed limits, location of stop lights/stop signs, lane divisions, school locations, etc. The map database contents can be produced or updated by a server connected through a wireless system in communication with the Internet, even as the vehicle 100 is driven along existing streets, yielding an up-to-date map.

The longitudinal planning system 348, when operating in L4 or L5 and based on sensor information from the external and interior vehicle sensors, can control the driving behavior of the vehicle in response to the current vehicle location, sensed object information, sensed vehicle occupant information, vehicle-related information, exterior environmental information, and navigation information from the maps database 335.

The sensed object information refers to sensed information regarding objects external to the vehicle. Examples include animate objects such as animals and attributes thereof (e.g., animal type, current spatial location, current activity, etc.), and pedestrians and attributes thereof (e.g., identity, age, sex, current spatial location, current activity, etc.), and the like and inanimate objects and attributes thereof such as other vehicles (e.g., current vehicle state or activity (parked or in motion or level of automation currently employed), occupant or operator identity, vehicle type (truck, car, etc.), vehicle spatial location, etc.), curbs (topography and spatial location), potholes (size and spatial location), lane division markers (type or color and spatial locations), signage (type or color and spatial locations such as speed limit signs, yield signs, stop signs, and other restrictive or warning signs), traffic signals (e.g., red, yellow, blue, green, etc.), buildings (spatial locations), walls (height and spatial locations), barricades (height and spatial location), and the like.

The sensed occupant information refers to sensed information regarding occupants internal to the vehicle. Examples include the number and identities of occupants and attributes thereof (e.g., seating position, age, sex, gaze direction, biometric information, authentication information, preferences, historic behavior patterns (such as current or historical user driving behavior, historical user route, destination, and waypoint preferences), nationality, ethnicity and race, language preferences (e.g., Spanish, English, Chinese, etc.), current occupant role (e.g., operator or passenger), occupant priority ranking (e.g., vehicle owner is given a higher ranking than a child occupant), electronic calendar information (e.g., Outlook™), and medical information and history, etc.

The vehicle-related information refers to sensed information regarding the selected vehicle. Examples include vehicle manufacturer, type, model, year of manufacture, current geographic location, current vehicle state or activity (parked or in motion or level of automation currently employed), vehicle specifications and capabilities, currently sensed operational parameters for the vehicle, and other information.

The exterior environmental information refers to sensed information regarding the external environment of the selected vehicle. Examples include road type (pavement, gravel, brick, etc.), road condition (e.g., wet, dry, icy, snowy, etc.), weather condition (e.g., outside temperature, pressure, humidity, wind speed and direction, etc.), ambient light conditions (e.g., time-of-day), degree of development of vehicle surroundings (e.g., urban or rural), and the like.

In a typical implementation, the automated longitudinal planning system 348, based on feedback from certain sensors, specifically the LIDAR and radar sensors positioned around the circumference of the vehicle, constructs a three-dimensional map in spatial proximity to the vehicle that enables the automated longitudinal planning system 348 to identify and spatially locate animate and inanimate objects. Other sensors, such as inertial measurement units, gyroscopes, wheel encoders, sonar sensors, motion sensors to perform odometry calculations with respect to nearby moving exterior objects, and exterior facing cameras (e.g., to perform computer vision processing) can provide further contextual information for generation of a more accurate three-dimensional map. The navigation information is combined with the three-dimensional map to provide short, intermediate and long-range course tracking and route selection. The longitudinal planning system 348 processes real-world information as well as GPS data, and driving speed to determine accurately the precise position of each vehicle, down to a few centimeters all while making corrections for nearby animate and inanimate objects.

The longitudinal planning system 348 can process in substantial real time the aggregate mapping information and models (or predicts) behavior of occupants of the current vehicle and other nearby animate or inanimate objects and, based on the aggregate mapping information and modeled behavior, issues appropriate commands regarding vehicle operation. While some commands are hard-coded into the vehicle, such as stopping at red lights and stop signs, other responses are learned and recorded by profile updates based on previous driving experiences. Examples of learned behavior include a slow-moving or stopped vehicle or emergency vehicle in a right lane suggests a higher probability that the car following it will attempt to pass, a pot hole, rock, or other foreign object in the roadway equates to a higher probability that a driver will swerve to avoid it, and traffic congestion in one lane means that other drivers moving in the same direction will have a higher probability of passing in an adjacent lane or by driving on the shoulder.

FIG. 4 shows one embodiment of the instrument panel 400 of the vehicle 100. The instrument panel 400 of vehicle 100 comprises a steering wheel 410, a vehicle operational display 420 (e.g., configured to present and/or display driving data such as speed, measured air resistance, vehicle information, entertainment information, etc.), one or more auxiliary displays 424 (e.g., configured to present and/or display information segregated from the operational display 420, entertainment applications, movies, music, etc.), a heads-up display 434 (e.g., configured to display any information previously described including, but in no way limited to, guidance information such as route to destination, or obstacle warning information to warn of a potential collision, or some or all primary vehicle operational data such as speed, resistance, etc.), a power management display 428 (e.g., configured to display data corresponding to electric power levels of vehicle 100, reserve power, charging status, etc.), and an input device 432 (e.g., a controller, touchscreen, or other interface device configured to interface with one or more displays in the instrument panel or components of the vehicle 100. The input device 432 may be configured as a joystick, mouse, touchpad, tablet, 3D gesture capture device, etc.). In some embodiments, the input device 432 may be used to manually maneuver a portion of the vehicle 100 into a charging position (e.g., moving a charging plate to a desired separation distance, etc.).

While one or more of displays of instrument panel 400 may be touch-screen displays, it should be appreciated that the vehicle operational display may be a display incapable of receiving touch input. For instance, the operational display 420 that spans across an interior space centerline 404 and across both a first zone 408A and a second zone 408B may be isolated from receiving input from touch, especially from a passenger. In some cases, a display that provides vehicle operation or critical systems information and interface may be restricted from receiving touch input and/or be configured as a non-touch display. This type of configuration can prevent dangerous mistakes in providing touch input where such input may cause an accident or unwanted control.

In some embodiments, one or more displays of the instrument panel 400 may be mobile devices and/or applications residing on a mobile device such as a smart phone. Additionally, or alternatively, any of the information described herein may be presented to one or more portions 420A-N of the operational display 420 or other display 424, 428, 434. In one embodiment, one or more displays of the instrument panel 400 may be physically separated or detached from the instrument panel 400. In some cases, a detachable display may remain tethered to the instrument panel.

The portions 420A-N of the operational display 420 may be dynamically reconfigured and/or resized to suit any display of information as described. Additionally, or alternatively, the number of portions 420A-N used to visually present information via the operational display 420 may be dynamically increased or decreased as required and are not limited to the configurations shown.

FIG. 5 illustrates a hardware diagram of communications componentry that can be optionally associated with the vehicle 100 in accordance with embodiments of the present disclosure.

The communications componentry can include one or more wired or wireless devices such as a transceiver(s) and/or modem that allows communications not only between the various systems disclosed herein but also with other devices, such as devices on a network, and/or on a distributed network such as the Internet and/or in the cloud and/or with other vehicle(s).

The communications subsystem 350 can also include inter- and intra-vehicle communications capabilities such as hotspot and/or access point connectivity for any one or more of the vehicle occupants and/or vehicle-to-vehicle communications.

Additionally, and while not specifically illustrated, the communications subsystem 350 can include one or more communications links (that can be wired or wireless) and/or communications busses (managed by the bus manager 574), including one or more of CANbus, OBD-II, ARCINC 429, Byteflight, CAN (Controller Area Network), D2B (Domestic Digital Bus), FlexRay, DC-BUS, IDB-1394, IEBus, I2C, ISO 9141-1/-2, J1708, J1587, J1850, J1939, ISO 11783, Keyword Protocol 2000, LIN (Local Interconnect Network), MOST (Media Oriented Systems Transport), Multifunction Vehicle Bus, SMARTwireX, SPI, VAN (Vehicle Area Network), and the like or in general any communications protocol and/or standard(s).

The various protocols and communications can be communicated one or more of wirelessly and/or over transmission media such as single wire, twisted pair, fiber optic, IEEE 1394, MIL-STD-1553, MIL-STD-1773, power-line communication, or the like. (All of the above standards and protocols are incorporated herein by reference in their entirety).

As discussed, the communications subsystem 350 enables communications between any of the inter-vehicle systems and subsystems as well as communications with non-collocated resources, such as those reachable over a network such as the Internet.

The communications subsystem 350, in addition to well-known componentry (which has been omitted for clarity), includes interconnected elements including one or more of: one or more antennas 504, an interleaver/deinterleaver 508, an analog front end (AFE) 512, memory/storage/cache 516, controller/microprocessor 520, MAC circuitry 522, modulator/demodulator 524, encoder/decoder 528, a plurality of connectivity managers 534, 558, 562, 566, GPU 540, accelerator 544, a multiplexer/demultiplexer 552, transmitter 570, receiver 572 and additional wireless radio components such as a Wi-Fi PHY/Bluetooth® module 580, a Wi-Fi/BT MAC module 584, additional transmitter(s) 588 and additional receiver(s) 592. The various elements in the device 350 are connected by one or more links/busses 5 (not shown, again for sake of clarity).

The device 350 can have one more antennas 504, for use in wireless communications such as multi-input multi-output (MIMO) communications, multi-user multi-input multi-output (MU-MIMO) communications Bluetooth®, LTE, 4G, 5G, Near-Field Communication (NFC), etc., and in general for any type of wireless communications. The antenna(s) 504 can include, but are not limited to one or more of directional antennas, omnidirectional antennas, monopoles, patch antennas, loop antennas, microstrip antennas, dipoles, and any other antenna(s) suitable for communication transmission/reception. In an exemplary embodiment, transmission/reception using MIMO may require particular antenna spacing. In another exemplary embodiment, MIMO transmission/reception can enable spatial diversity allowing for different channel characteristics at each of the antennas. In yet another embodiment, MIMO transmission/reception can be used to distribute resources to multiple users for example within the vehicle 100 and/or in another vehicle.

Antenna(s) 504 generally interact with the Analog Front End (AFE) 512, which is needed to enable the correct processing of the received modulated signal and signal conditioning for a transmitted signal. The AFE 512 can be functionally located between the antenna and a digital baseband system in order to convert the analog signal into a digital signal for processing and vice-versa.

The subsystem 350 can also include a controller/microprocessor 520 and a memory/storage/cache 516. The subsystem 350 can interact with the memory/storage/cache 516 which may store information and operations necessary for configuring and transmitting or receiving the information described herein. The memory/storage/cache 516 may also be used in connection with the execution of application programming or instructions by the controller/microprocessor 520, and for temporary or long-term storage of program instructions and/or data. As examples, the memory/storage/cache 520 may comprise a computer-readable device, RAM, ROM, DRAM, SDRAM, and/or other storage device(s) and media.

The controller/microprocessor 520 may comprise a general-purpose programmable processor or controller for executing application programming or instructions related to the subsystem 350. Furthermore, the controller/microprocessor 520 can perform operations for configuring and transmitting/receiving information as described herein. The controller/microprocessor 520 may include multiple processor cores, and/or implement multiple virtual processors. Optionally, the controller/microprocessor 520 may include multiple physical processors. By way of example, the controller/microprocessor 520 may comprise a specially configured Application Specific Integrated Circuit (ASIC) or other integrated circuit, a digital signal processor(s), a controller, a hardwired electronic or logic circuit, a programmable logic device or gate array, a special purpose computer, or the like.

The subsystem 350 can further include a transmitter(s) 570, 588 and receiver(s) 572, 592 which can transmit and receive signals, respectively, to and from other devices, subsystems and/or other destinations using the one or more antennas 504 and/or links/busses. Included in the subsystem 350 circuitry is the medium access control or MAC Circuitry 522. MAC circuitry 522 provides for controlling access to the wireless medium. In an exemplary embodiment, the MAC circuitry 522 may be arranged to contend for the wireless medium and configure frames or packets for communicating over the wired/wireless medium.

The subsystem 350 can also optionally contain a security module (not shown). This security module can contain information regarding but not limited to, security parameters required to connect the device to one or more other devices or other available network(s), and can include WEP or WPA/WPA-2 (optionally+AES and/or TKIP) security access keys, network keys, etc. The WEP security access key is a security password used by Wi-Fi networks. Knowledge of this code can enable a wireless device to exchange information with an access point and/or another device. The information exchange can occur through encoded messages with the WEP access code often being chosen by the network administrator. WPA is an added security standard that is also used in conjunction with network connectivity with stronger encryption than WEP.

In some embodiments, the communications subsystem 350 also includes a GPU 540, an accelerator 544, a Wi-Fi/BT/BLE (Bluetooth® Low-Energy) PHY module 580 and a Wi-Fi/BT/BLE MAC module 584 and optional wireless transmitter 588 and optional wireless receiver 592. In some embodiments, the GPU 540 may be a graphics processing unit, or visual processing unit, comprising at least one circuit and/or chip that manipulates and changes memory to accelerate the creation of images in a frame buffer for output to at least one display device. The GPU 540 may include one or more of a display device connection port, printed circuit board (PCB), a GPU chip, a metal-oxide-semiconductor field-effect transistor (MOSFET), memory (e.g., single data rate random-access memory (SDRAM), double data rate random-access memory (DDR) RAM, etc., and/or combinations thereof), a secondary processing chip (e.g., handling video out capabilities, processing, and/or other functions in addition to the GPU chip, etc.), a capacitor, heatsink, temperature control or cooling fan, motherboard connection, shielding, and the like.

The various connectivity managers 534, 558, 562, 566 manage and/or coordinate communications between the subsystem 350 and one or more of the systems disclosed herein and one or more other devices/systems. The connectivity managers 534, 558, 562, 566 include a charging connectivity manager 534, a vehicle database connectivity manager 558, a remote operating system connectivity manager 562, and a sensor connectivity manager 566.

The charging connectivity manager 534 can coordinate not only the physical connectivity between the vehicle 100 and a charging device/vehicle but can also communicate with one or more of a power management controller, one or more third parties, and optionally a billing system(s). As an example, the vehicle 100 can establish communications with the charging device/vehicle to one or more of coordinate interconnectivity between the two (e.g., by spatially aligning the charging receptacle on the vehicle with the charger on the charging vehicle) and optionally share navigation information. Once charging is complete, the amount of charge provided can be tracked and optionally forwarded to, for example, a third party for billing. In addition to being able to manage connectivity for the exchange of power, the charging connectivity manager 534 can also communicate information, such as billing information to the charging vehicle and/or a third party. This billing information could be, for example, the owner of the vehicle, the driver/occupant(s) of the vehicle, company information, or in general any information usable to charge the appropriate entity for the power received.

The vehicle database connectivity manager 558 allows the subsystem to receive and/or share information stored in the vehicle database. This information can be shared with other vehicle components/subsystems and/or other entities, such as third parties and/or charging systems. The information can also be shared with one or more vehicle occupant devices, such as an app (application) on a mobile device the driver uses to track information about the vehicle 100 and/or a dealer or service/maintenance provider. In general, any information stored in the vehicle database can optionally be shared with any one or more other devices optionally subject to any privacy or confidentially restrictions.

The remote operating system connectivity manager 562 facilitates communications between the vehicle 100 and any one or more autonomous vehicle systems. These communications can include one or more of navigation information, vehicle information, other vehicle information, weather information, occupant information, or in general any information related to the remote operation of the vehicle 100.

The sensor connectivity manager 566 facilitates communications between any one or more of the vehicle sensors (e.g., the driving vehicle sensors and systems 304, etc.) and any one or more of the other vehicle systems. The sensor connectivity manager 566 can also facilitate communications between any one or more of the sensors and/or vehicle systems and any other destination, such as a service company, app, or in general to any destination where sensor data is needed.

In accordance with one exemplary embodiment, any of the communications discussed herein can be communicated via the conductor(s) used for charging. One exemplary protocol usable for these communications is Power-line communication (PLC). PLC is a communication protocol that uses electrical wiring to simultaneously carry both data, and Alternating Current (AC) electric power transmission or electric power distribution. It is also known as power-line carrier, power-line digital subscriber line (PDSL), mains communication, power-line telecommunications, or power-line networking (PLN). For DC environments in vehicles PLC can be used in conjunction with CAN-bus, LIN-bus over power line (DC-LIN) and DC-BUS.

The communications subsystem can also optionally manage one or more identifiers, such as an IP (Internet Protocol) address(es), associated with the vehicle and one or other system or subsystems or components and/or devices therein. These identifiers can be used in conjunction with any one or more of the connectivity managers as discussed herein.

FIG. 6 illustrates a block diagram of a computing environment 600 that may function as the servers, user computers, or other systems provided and described herein. The computing environment 600 includes one or more user computers, or computing devices, such as a vehicle computing device 604, a communication device 608, and/or more 612. The computing devices 604, 608, 612 may include general purpose personal computers (including, merely by way of example, personal computers, and/or laptop computers running various versions of Microsoft Corp.'s Windows® and/or Apple Corp.'s Macintosh® operating systems) and/or workstation computers running any of a variety of commercially-available UNIX® or UNIX-like operating systems. These computing devices 604, 608, 612 may also have any of a variety of applications, including for example, database client and/or server applications, and web browser applications. Alternatively, the computing devices 604, 608, 612 may be any other electronic device, such as a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via a network 352 and/or displaying and navigating web pages or other types of electronic documents or information. Although the exemplary computing environment 600 is shown with two computing devices, any number of user computers or computing devices may be supported.

The computing environment 600 may also include one or more servers 614, 616. In this example, server 614 is shown as a web server and server 616 is shown as an application server. The web server 614, which may be used to process requests for web pages or other electronic documents from computing devices 604, 608, 612. The web server 614 can be running an operating system including any of those discussed above, as well as any commercially-available server operating systems. The web server 614 can also run a variety of server applications, including SIP (Session Initiation Protocol) servers, HTTP(s) servers, FTP servers, CGI servers, database servers, Java® servers, and the like. In some instances, the web server 614 may publish operations available operations as one or more web services.

The computing environment 600 may also include one or more file and or/application servers 616, which can, in addition to an operating system, include one or more applications accessible by a client running on one or more of the computing devices 604, 608, 612. The server(s) 616 and/or 614 may be one or more general purpose computers capable of executing programs or scripts in response to the computing devices 604, 608, 612. As one example, the server 616, 614 may execute one or more web applications. The web application may be implemented as one or more scripts or programs written in any programming language, such as Java®, C, C#®, or C++, and/or any scripting language, such as Perl, Python, or TCL, as well as combinations of any programming/scripting languages. The application server(s) 616 may also include database servers, including without limitation those commercially available from Oracle®, Microsoft®, Sybase®, IBM® and the like, which can process requests from database clients running on a computing device 604, 608, 612.

The web pages created by the server 614 and/or 616 may be forwarded to a computing device 604, 608, 612 via a web (file) server 614, 616. Similarly, the web server 614 may be able to receive web page requests, web services invocations, and/or input data from a computing device 604, 608, 612 (e.g., a user computer, etc.) and can forward the web page requests and/or input data to the web (application) server 616. In further embodiments, the server 616 may function as a file server. Although for ease of description, FIG. 6 illustrates a separate web server 614 and file/application server 616, those skilled in the art will recognize that the functions described with respect to servers 614, 616 may be performed by a single server and/or a plurality of specialized servers, depending on implementation-specific needs and parameters. The computer systems 604, 608, 612, web (file) server 614 and/or web (application) server 616 may function as the system, devices, or components described in FIGS. 1-6.

The computing environment 600 may also include a database 618. The database 618 may reside in a variety of locations. By way of example, database 618 may reside on a storage medium local to (and/or resident in) one or more of the computers 604, 608, 612, 614, 616. Alternatively, it may be remote from any or all of the computers 604, 608, 612, 614, 616, and in communication (e.g., via the network 352) with one or more of these. The database 618 may reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers 604, 608, 612, 614, 616 may be stored locally on the respective computer and/or remotely, as appropriate. The database 618 may be a relational database, such as Oracle 20i®, that is adapted to store, update, and retrieve data in response to SQL-formatted commands.

FIG. 7 illustrates one embodiment of a computer system 700 upon which the servers, user computers, computing devices, or other systems or components described above may be deployed or executed. The computer system 700 is shown comprising hardware elements that may be electrically coupled via a bus 704. The hardware elements may include one or more central processing units (CPUs) 708; one or more input devices 712 (e.g., a mouse, a keyboard, etc.); and one or more output devices 716 (e.g., a display device, a printer, etc.). The computer system 700 may also include one or more storage devices 720. By way of example, storage device(s) 720 may be disk drives, optical storage devices, solid-state storage devices such as a random-access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like.

The computer system 700 may additionally include a computer-readable storage media reader 724; a communications system 728 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.); and working memory 736, which may include RAM and ROM devices as described above. The computer system 700 may also include a processing acceleration unit 732, which can include a DSP, a special-purpose processor, and/or the like.

The computer-readable storage media reader 724 can further be connected to a computer-readable storage medium, together (and, optionally, in combination with storage device(s) 720) comprehensively representing remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing computer-readable information. The communications system 728 may permit data to be exchanged with a network and/or any other computer described above with respect to the computer environments described herein. Moreover, as disclosed herein, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random-access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine-readable mediums for storing information.

The computer system 700 may also comprise software elements, shown as being currently located within a working memory 736, including an operating system 740 and/or other code 744. It should be appreciated that alternate embodiments of a computer system 700 may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed.

Examples of the processors 340, 708 as described herein may include, but are not limited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bit computing, Apple® A7 processor with 64-bit architecture, Apple® M7 motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family of processors, the Intel® Xeon® family of processors, the Intel® Atom™ family of processors, the Intel Itanium® family of processors, Intel® Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nm Ivy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300, and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments® Jacinto C6000™ automotive infotainment processors, Texas Instruments® OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors, ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalent processors, and may perform computational functions using any known or future-developed standard, instruction set, libraries, and/or architecture.

What is needed is a longitudinal planning system capable of automatically planning and executing a smooth and safe velocity change maneuver. For example, an autonomous or semi-autonomous vehicle driving in traffic may be capable of determining a velocity change maneuver is optimal compared to a current state of the vehicle.

As used herein, ego vehicle refers to the vehicle containing the longitudinal planning system being described while obstacle vehicle refers to any other vehicle.

The range of reachable velocities of the vehicle may be limited by upper and lower limits on the acceleration of the vehicle. Note that acceleration is not necessarily positive and thus the lower limit on the acceleration may be a negative acceleration and thus may be described as deceleration. As such, the lower limit may be a negative rate of acceleration, for example the vehicle may coast or slightly brake to reduce its rate of speed. The upper limit may be a positive rate of acceleration, for example the vehicle may increase its speed.

The upper and lower acceleration limits may be vehicle-dependent. The limits may depend on factors such as driver and passenger comfort and safety, road conditions, vehicle capabilities, etc.

As described herein, the velocity change maneuver should be optimal based on a consideration of a number of factors. For example, the longitudinal planning system should optimize obstacle avoidance. Obstacles to avoid may comprise other moving vehicles. The vehicle may store and update a database of nearby vehicles and track data on such nearby vehicles. For example, a vehicle-obstacle database may track data associated with other vehicles including an identification of a lane in which each obstacle vehicle is travelling, a speed of each obstacle vehicle, an acceleration of each obstacle vehicle, a probability of an upcoming lane change of each obstacle vehicle, etc. The longitudinal planning system may also track non-moving obstacles such as street signs, construction work, fences, etc.

A velocity change maneuver may also be chosen based on a speed required for the maneuver. Factors affecting an optimal speed may include, but are not limited to, other vehicles, speed limits, weather and/or driving conditions, upcoming turns according to a current route of the vehicle, ideal acceleration and/or deceleration limits, or other factors.

Memory System

As illustrated in FIG. 7, the longitudinal planning system may be in communication with a memory element such as a storage device 720 storing one or more databases. In some embodiments, a database may track one or more moving obstacle vehicles. For example, as illustrated in FIG. 8A, the ego vehicle 8000 may be driving in a third lane 8023 of a five-lane highway. As illustrated by the x/y axis 8030, the vehicles may be travelling in a +y direction. As used herein, the term longitudinal distance may refer to a distance traveled by a vehicle in a straight line parallel to the lane lines. In the example illustrated in FIG. 8A, longitudinal distance may refer to a distance parallel to the y-axis.

A number of obstacle vehicles 8001-8006 may be driving near the ego vehicle 8000 in one or more of the five lanes 8021-8025.

A goal of the longitudinal planning system may be to represent all possible lateral states and dynamics of the ego vehicle while conserving memory and processor requirements. To this aim, the longitudinal planning system may use local identifiers and measure qualities of obstacle vehicles based relative to the ego vehicle. In some embodiments, a speed of an obstacle vehicle may be measured as relative to the ego vehicle. For example, if the ego vehicle is travelling at 60 mph and a first obstacle vehicle is travelling at 62 mph, the ego vehicle may record the obstacle vehicle's speed as +2 mph.

The ego vehicle 8000 may sense information associated with one or more of the obstacle vehicles 8001-8006. For example, the ego vehicle 8000 may determine a current lane, a speed, and/or an acceleration/deceleration of an obstacle vehicle. In some embodiments, values such as current lane, speed, acceleration, etc. may be determined in comparison to the ego vehicle 8000. For example, if a first obstacle vehicle 8001 is traveling at 1% faster than the ego vehicle 8000, the ego vehicle 8000 may record the speed of the first obstacle vehicle 8001 as 1.01. Similarly, if another obstacle vehicle 8004 is traveling 5% slower than the ego vehicle 8000, the ego vehicle 8000 may record the speed of the other obstacle vehicle 8004 as 0.95.

Likewise, acceleration of obstacle vehicles 8001-8006 may be determined in comparison to the ego vehicle 8000. For example, an acceleration of 0.0 may represent a vehicle is accelerating at a same rate as the ego vehicle 8000. In some embodiments, acceleration may be measured in an actual value as opposed to as relative to the ego vehicle 8000. In some embodiments, if a first obstacle vehicle 8001 is accelerating at the same rate as the ego vehicle 8000, the ego vehicle 8000 may record the acceleration of the first obstacle vehicle 8001 as 0.0. Similarly, if a second obstacle vehicle 8002 is accelerating at a 10% greater rate than the ego vehicle 8000, the ego vehicle 8000 may record the acceleration of the other obstacle vehicle 8002 as 0.1.

The ego vehicle 8000 may also track a current lane 8021-8025 for one or more of the obstacle vehicles 8001-8006. In some embodiments, the ego vehicle 8000 may only track statistics of vehicle in the ego lane 8023 as well as the two neighboring lanes 8022, 8024. By limiting the tracked vehicles to those lanes, the memory and processor requirements for analyzing proper lane changes may be reduced. Lane identifiers used by the ego vehicle 8000 may be recorded by the ego vehicle 8000 based on a relative position of each lane to the ego vehicle 8000. For example, the ego lane 8022 containing the ego vehicle 8000 may be labelled as Lane 00. The lane 8022 to the immediate left of the ego vehicle 8000 may be referred to as Lane 10. The lane 8024 to the immediate right of the ego vehicle 8000 may be referred to as Lane 01. In some embodiments, the ego vehicle may store data associated only with obstacle vehicles in lanes 00, 01, and 10.

The ego vehicle 8000 may also determine a location of one or more of the obstacle vehicles 8001-8006. In some embodiments, the location may simply be a determination of a distance between a nearest edge of the ego vehicle 8000 and one of the obstacle vehicles 8001-8006. For simplicity, the system may assume that any obstacle vehicle 8001-8006 occupies the total width of its lane 8021-8025. The distance may, in some embodiments, be simply a measurement along the y-axis. As illustrated in FIG. 8A, the ego vehicle 8000 may determine a distance 8011 between the front bumper of the ego vehicle 8000 and the rear bumper of the obstacle vehicle 8001 directly in front of the ego vehicle 8000. As used herein, a position of an obstacle may comprise both of a lane ID and a distance of the obstacle from the ego vehicle.

Distance of each obstacle may be measured in terms of a relative distance from the nearest edge of the ego vehicle. For example, in the case of a leading car ahead of the ego vehicle in the same lane, the position of the leading car may be measured in a distance from the front bumper of the ego car to the back bumper of the leading car. Similarly, for an obstacle in a lane adjacent to the ego lane driving ahead of the ego vehicle, the position distance of the obstacle may be measured simply in terms of the longitudinal distance from the front bumper of the ego vehicle to the obstacle.

The ego vehicle 8000 may determine a distance 8012 between the ego vehicle 8000 and an obstacle vehicle 8002 in a neighboring lane 8024. If the obstacle vehicle 8002 is at least slightly leading the ego vehicle 8000, the distance 8012 may be measured from the front bumper of the ego vehicle 8000 and the rear bumper of the obstacle vehicle 8002. The distance may, in some embodiments, be simply a measurement along the y-axis to simplify the calculations required.

Similarly, the ego vehicle 8000 may determine a distance 8014 between the ego vehicle 8000 and an obstacle vehicle 8004 behind the ego vehicle 8000 in a neighboring lane 8024. If the obstacle vehicle 8004 is at least slightly behind the ego vehicle 8000, the distance 8014 may be measured from the rear bumper of the ego vehicle 8000 and the front bumper of the obstacle vehicle 8004.

In some embodiments, if an obstacle vehicle 8003 in a neighboring lane 8022 is neither completely ahead of or completely behind the ego vehicle 8000, the ego vehicle 8000 may record the location of the obstacle vehicle 8003 as a negative number associated with the distance 8013 between the front bumper of the ego vehicle 8000 and the back bumper of the neighboring obstacle vehicle 8003.

As illustrated in FIG. 8B, a table 8040 may be stored in memory and may be actively updated during travel. The table 8040 may store data for each of the currently-monitored obstacle vehicles. To continue the example illustrated in FIG. 8A, the ego vehicle 8000 may monitor statistics for any vehicle in the ego lane 8023, the lane 8022 to the left of the ego lane 8023 and the lane 8024 to the right of the ego lane 8023. As illustrated in FIGS. 8A and 8B, the table 8040 may comprise data for each of obstacles 8001-8004. The table may store data for each of the obstacles 8001-8004 such as an obstacle ID, a lane ID of a lane containing each obstacle, a relative speed, a relative rate of acceleration, a relative distance, and an indicator as to whether the distance is measured from the front or rear/back bumper of the ego vehicle 8000.

A longitudinal planning system may be capable of determining a vehicle's current state in terms of its speed, distance from a leader vehicle, and the speed of the leader vehicle. A longitudinal planning system may determine a control scheme for maneuvering the vehicle given the vehicle's current status. For example, if the vehicle is following behind a leader or obstacle vehicle, the longitudinal planning system may determine accelerating or decelerating the vehicle at a particular rate may be preferred.

The longitudinal planning system may also be capable of automatically carrying out a maneuver given a specific control scheme. For example, the longitudinal planning system may determine a specific moment to initiate the maneuver and at what rate to change the acceleration of the vehicle.

In some embodiments, a vehicle may have an optimal maximum and minimum rate of acceleration for a maneuver. The optimal maximum and minimum rates of acceleration may be determined based on a number of factors, such as safety, comfort, etc. Note that the optimal maximum acceleration may not be the total possible maximum acceleration of the vehicle and the optimal minimum acceleration may not be the total possible minimum acceleration (which would be hitting the brakes to slow the vehicle as quickly as possible). In some embodiments, the optimal minimum acceleration may equivalent to a gentle pressing of the brakes.

With regards to FIGS. 9A and 9B, note that the input is the relative acceleration a_(ego)-a_(leader). The maximum relative acceleration can have arbitrary sign, but the longitudinal planning system may define assumptions such that the minimum relative acceleration is always negative to simplify collision avoidance. That is, the ego vehicle must be capable of decelerating faster than the leader vehicle so as be capable of avoiding collision.

The states (distance and relative speed) for the problem are defined relative to the leader, and the leader's known acceleration (which may be assumed to be 0) may be factored in accordingly by defining the control input to this system as the relative acceleration a_(rel)=a_(ego)-a_(leader).

Because these input bounds change with the leader acceleration, the longitudinal planning system may re-solve the system on each piecewise constant acceleration segment of the leader's speed profile. That is, the longitudinal planning system may solve for how to converge to the leader and maintain the desired distance indefinitely (where the steady-state is held by matching the leader's acceleration) and apply this policy until the leader's acceleration changes. At this transition to the next segment in the leader's speed profile, the longitudinal planning system may identify the new relative acceleration bounds and again solve for the bang-bang speed profile given the new initial conditions.

Every state is reachable (can be reached by the ego vehicle from the origin in some amount of time) under bang-bang control if the acceleration limits allow both positive and negative accelerations. Typically, the longitudinal planning system may assume boundaries on the absolute acceleration to be a_(min)≤a_(ego)≤a_(max) where a_(min)<0 and a_(max)>0. However, dealing with calculations of the ego vehicles acceleration relative to a leader vehicle's acceleration may be more difficult as there is no guarantee of the range of the relative acceleration, due to the dependency on the leader's acceleration. To guarantee the ego vehicle's ability to reduce its relative speed (for collision avoidance), the longitudinal planning system may make the somewhat artificial assumption that each car can always decelerate faster than its most proximal leader. Thus, in some cases if the acceleration of the leader becomes less than a_(min), the longitudinal planning system may adjust a_(min) to be equal to a_(leader)−a_(min rel) for some minimum negative relative acceleration. This adjustment may result in the final lower bound of a_(rel) being negative.

The speed profiles or control schemes to be executed by the longitudinal planning system in order to follow a leading vehicle at an optimal following distance may be plotted on a phase plot as illustrated in FIG. 9A.

In FIG. 9A, a two-axis graph 900 is illustrated. A vertical axis 903 may illustrate a relative velocity of the ego vehicle compared to a leading vehicle. The vertical axis 903 may plot the ego vehicle velocity minus the leading vehicle velocity. As such, the higher a point is on the vertical axis 903, the faster the ego vehicle is travelling compared to the leading vehicle. Points along the horizontal axis 906 may represent the ego and leading vehicles travelling at a same speed.

Points below the horizontal axis 906 may represent the ego vehicle travelling slower than the leading vehicle.

The horizontal axis 906 may plot a distance between the ego and leading vehicles. The further to the right a point is on the graph 900 the further the ego vehicle is from the leading vehicle. The further to the left a vehicle is on the graph 900 the closer the ego vehicle is to the leading vehicle. Points along the vertical axis 903 may represent the ego vehicle physically touching the leading vehicle if the ego and leading vehicles are in the same lane. The graph may also be used to represent a situation with a leading vehicle in a lane other than the ego lane, in which case points along the vertical axis 903 may represent the ego vehicle beginning to pass the leading vehicle.

An origin of the graph 900 shown by the intersection of the horizontal axis 906 and the vertical axis 903 may represent the ego vehicle physically touching the leading vehicle, if travelling in the same lane, while travelling at the same speed.

Points in the upper left quadrant, above the horizontal axis 906 and to the left of the vertical axis 903 may represent the ego vehicle travelling at a faster speed compared to the leading vehicle without any distance between the ego and leading vehicles. If the ego and leading vehicles are in the same lane, points in the upper left quadrant may represent a collision.

Points in the upper right quadrant, above the horizontal axis 906 and to the right of the vertical axis 903 may represent the ego vehicle travelling at a faster speed compared to the leading vehicle with a positive distance between the ego and leading vehicles.

Points in the lower right quadrant, below the horizontal axis 906 and to the right of the vertical axis 903 may represent the ego vehicle travelling at a lower speed compared to the leading vehicle with a positive distance between the ego and leading vehicles.

Points in the lower left quadrant, below the horizontal axis 906 and to the left of the vertical axis 903 may represent the ego vehicle travelling at a lower speed compared to the leading vehicle without any distance between the ego and leading vehicles. If the ego and leading vehicles are in the same lane, points in the lower left quadrant may represent a collision.

To execute a velocity change maneuver in a scenario in which there is a leading vehicle in either the ego lane or a target lane, the longitudinal planning system of the ego vehicle may determine an optimal following distance to follow behind the leading vehicle. An optimal following distance should be on the right side of the vertical axis 903, representing a positive distance between the ego vehicle and the leading vehicle.

In some circumstances, the longitudinal planning system may determine a rate of speed equal to that of the leading vehicle is optimal to execute a velocity change maneuver. In such circumstances, the optimal position for the ego vehicle will be on the horizontal axis 906 and to the right of the vertical axis 903.

Arrows 909, 912, 915, 918, 921, 924, 927, 930, 933, and 936 represent vector goal trajectories for an ego vehicle to reach an optimal following distance represented by the point 939 indicated in FIG. 9A.

For example, a longitudinal planning system of an ego vehicle at the tail end of the arrow 915 may determine the ego vehicle is too close to the leading vehicle and travelling too fast in comparison to the leading vehicle. In response to such a decision, the longitudinal planning system may determine a proper action is to decelerate at a maximum deceleration, effectively hitting the brakes. As illustrated by the direction of the arrow 915, the relative speed should greatly decrease while the distance between the vehicles should remain the same.

A vehicle at the tail end of the arrow 918 is travelling slower than the leading vehicle but is still too close to the leading vehicle. In such a scenario, the longitudinal planning system may determine a moderate deceleration is appropriate as indicated by the arrow 918.

A vehicle at the tail end of the arrow 921 is travelling slower than the leading vehicle but is closer to the leading vehicle than the optimal following distance 939. In such a scenario, the longitudinal planning system may determine maintaining a same velocity differential compared to the leading vehicle is appropriate as indicated by the arrow 921. For example, if both the ego and leading vehicles are driving at constant speeds, and the ego vehicle is driving slightly slower than the leading vehicle, the ego vehicle may continue driving at the slower but constant speed until reaching a point near the tail end of the arrow 924.

A vehicle at the tail end of the arrow 924 is travelling slower than the leading vehicle but is now further from the leading vehicle than the optimal distance 939. In such a scenario, the longitudinal planning system may determine a moderate acceleration is appropriate as indicated by the arrow 924.

A vehicle at the tail end of the arrow 927 is travelling slightly slower than the leading vehicle and is still too far from the leading vehicle. In such a scenario, the longitudinal planning system may apply a moderate acceleration to follow the trajectory as indicated by the arrow 927.

A vehicle at the tail end of the arrow 930 is travelling slightly faster than the leading vehicle but is still too far from the leading vehicle. In such a scenario, the longitudinal planning system may alter the acceleration of the vehicle to follow the trajectory indicated by the arrow 930.

A vehicle at the tail end of the arrow 933 is travelling faster than the leading vehicle but is still too far from the leading vehicle. In such a scenario, the longitudinal planning system may alter the acceleration of the vehicle to follow the trajectory indicated by the arrow 930.

A vehicle at the tail end of the arrow 936 is travelling faster than the leading vehicle but is still too far from the leading vehicle. In such a scenario, the longitudinal planning system may alter the acceleration of the vehicle to follow the trajectory indicated by the arrow 936.

A vehicle at the tail end of the arrow 909 is travelling much faster than the leading vehicle but is relatively close to the optimal following distance 939. In such a scenario, the longitudinal planning system may alter the acceleration of the vehicle to follow the trajectory indicated by the arrow 909.

A vehicle at the tail end of the arrow 912 is travelling much faster than the leading vehicle but is relatively close to the optimal following distance 939. In such a scenario, the longitudinal planning system may alter the acceleration of the vehicle to follow the trajectory indicated by the arrow 912.

A more detailed trajectory phase plane chart 950 is illustrated in FIG. 9B. As can be appreciated, the acceleration plans executed by the longitudinal planning system may be gradually executed as illustrated by the curved vector arrows. In addition to determining an optimal following distance 939, the longitudinal planning system may also determine a critical following distance 962. The critical distance 962 may be selected from distances which are less than optimal but relatively safe. Vehicles at distances between the optimal following distance 939 and the critical distance 962 may be capable of returning to the optimal distance using relatively moderate acceleration profiles.

For example, the longitudinal planning system may adjust the acceleration of the vehicle between a minimum and a maximum acceleration. During normal operation of the vehicle, the longitudinal planning system may set the minimum acceleration to a higher amount than is physically possible. The minimum acceleration may be determined based on comfort for the driver and passengers, road quality, weather conditions, safety, etc.

Similarly, the maximum acceleration may be set to a lower amount than is physically possible. The maximum may also be determined based on comfort for the driver and passengers, road quality, weather conditions, safety, etc.

In most situations, it may be possible to stay within the normal maximum and minimum boundaries for the acceleration.

For example, as illustrated in FIG. 9B, a vehicle at positions reflected by the tail end of arrows may be capable of approaching the optimal following distance by applying the minimum acceleration. Similarly, a vehicle at positions reflected by the tail end of arrows may be capable of approaching the optimal following distance by applying the maximum acceleration. However, a vehicle at positions reflected by the tail end of arrows may not be capable of approaching the optimal following distance by staying within the normal maximum and minimum acceleration boundaries. Such a vehicle may be required to apply a total maximum or total minimum acceleration to avoid a collision with a leading vehicle.

As can be appreciated, in most scenarios, an ego vehicle may approach an optimal following distance using a relatively simple control scheme. By choosing a maximum and minimum acceleration for the vehicle to be used in normal operating conditions, the processor and memory requirements to determine a proper speed profile to arrive at a particular following distance may be reduced.

As described above, every state is reachable (can be reached by the ego vehicle from the origin in some amount of time) under bang-bang control if the acceleration limits allow both positive and negative accelerations. Typically, the longitudinal planning system may assume boundaries on the absolute acceleration to be a_(min)≤a_(go)≤a_(max) where a_(min)<0 and a_(max)>0. However, dealing with calculations of the ego vehicles acceleration relative to a leader vehicle's acceleration may be more difficult as there is no guarantee of the range of the relative acceleration, a_(rel) due to the dependency on the leader's acceleration. To guarantee the ego vehicle's ability to reduce its relative speed (for collision avoidance), the longitudinal planning system may make the somewhat artificial assumption that each car can always decelerate faster than its most proximal leader. Thus, in some cases if the acceleration of the leader becomes less than a_(min), the longitudinal planning system may adjust a_(min) to be equal to a_(leader)−a_(min rel) for some minimum negative relative acceleration. This adjustment may result in the final lower bound of a_(rel) being negative.

Meanwhile, there may not be a practical need to guarantee the ego vehicle's ability to accelerate faster than the leader, it is possible to have the maximum a_(rel) be zero or even less than zero (when the leader is accelerating quickly). As accelerating slower than a leading vehicle may not lead to a collision or other dangerous situation. This allows for the basic nature of the bang-bang phase portrait to be modified and the equilibrium conditions may be changed. If the relative maximum acceleration (the acceleration of the leader minus the maximum acceleration of the ego vehicle) is zero, everything below the horizontal axis is an equilibrium in that the relative speed is constant (but the distance between the vehicles is always increasing as the ego vehicle is travelling slower than the leading vehicle). If the maximum relative acceleration is less than zero, i.e., the leading vehicle is accelerating at a greater rate than the ego vehicle is capable of, everything below the horizontal axis is a steady-state in that the ego vehicle may hold its maximum acceleration indefinitely, but the speed difference is always decreasing and the distance is always increasing. Both of these cases require specific modifications to the algebraic solver of the bang-bang system (the former is a linear system that is singular if represented quadratically, the latter is an “inverted” version whose relevant root is the opposite of the standard system).

In some situations, the leading vehicle may be accelerating at the same rate as the maximum acceleration of the ego vehicle. This maximum acceleration may be the ego vehicle's total maximum acceleration or may be the maximum acceleration for normal operation of the vehicle which may be less than the ego vehicle's total maximum acceleration.

As illustrated in FIG. 9C, a vehicle may use a phase portrait to follow a motion plan. The phase portrait may plot a series of possible motion plans. Motion plans may plot a trajectory for the ego vehicle to follow given a distance between the ego vehicle and an ideal following distance from the vehicle. The distance from the ego vehicle to the ideal following distance may be plotted on an x-axis as illustrated in FIG. 9C. The difference between the ego vehicle and the leader vehicle may be plotted on a y-axis. The vehicle control system may determine the ego vehicle's position on the phase portrait and may then use the motion plan for that position to determine a trajectory for the ego vehicle to follow.

In the phase portrait of FIG. 9C, the ideal following distance of the ego vehicle is 8.6 meters behind the leader vehicle. This value should be considered as given for example purposes only.

As illustrated in FIG. 9D, an ego vehicle with an ideal following distance of 40 m following a leader vehicle traveling at 25 m/s may use a similar phase portrait to follow a motion plan as discussed above with relation to FIG. 9C. Note that in FIG. 9D, the y-axis plots the actual speed of the ego vehicle as opposed to the relative speed of the ego vehicle.

Similarly, as illustrated in FIG. 9E, a vehicle may use a phase portrait of LQR trajectories. LQR trajectories may be used by an LQR controller to control the trajectory of the vehicle.

And as illustrated in FIG. 9F, an ego vehicle with an ideal following distance of 40 m following a leader vehicle traveling at 25 m/s may use a similar phase portrait of LQR trajectories to control the trajectory of the vehicle. Note that in FIG. 9F, the y-axis plots the actual speed of the ego vehicle as opposed to the relative speed of the ego vehicle.

As illustrated in FIG. 9G, a phase portrait may comprise undershoot and/or overshoot constraints for the ego vehicle to follow. For example, if the state of the vehicle is within the boundaries set by the undershoot and overshoot constraint lines of the phase portrait as illustrated in FIG. 9G, the vehicle may follow a typical control plan. If the vehicle crosses a constraint line, the vehicle may follow a different control plan. For example, if the vehicle state is above the undershoot constraint line of the phase portrait, the vehicle control system may determine the vehicle is too close to the leader vehicle given the speed difference between the leader and ego vehicles. Similarly, if the vehicle state is below the overshoot constraint line, the vehicle control system may determine the ego vehicle is traveling too slow given the current scenario.

As illustrated in FIG. 9H, an ego vehicle with an ideal following distance of 40 m following a leader vehicle traveling at 25 m/s may use similar undershoot and/or overshoot constraints for the ego vehicle to follow as discussed above with FIG. 9G. Note that in FIG. 9H, the y-axis plots the actual speed of the ego vehicle as opposed to the relative speed of the ego vehicle.

A vehicle control system may utilize a braking leader critical constraint line as illustrated in FIG. 9. Again, the x-axis of the phase portrait illustrated in FIG. 9I may plot a distance from the ego vehicle and the leader vehicle. The y-axis of FIG. 9I may plot a relative speed of the ego vehicle as compared to the leader vehicle. The vehicle control system may have a custom value for the proper following distance. In the example as illustrated in FIG. 9I, the distance is set at 8.6 m. A braking leader critical constraint line may set a barrier from which if the ego vehicle crosses above the braking leader critical constraint line the control system may determine the ego vehicle may not be capable of stopping in time if the leader vehicle applies its brakes to a maximum amount. As such, the vehicle control system may follow a drastic control plan in such an event that the state of the ego vehicle is above the braking leader critical constraint line.

As illustrated in FIG. 9J, an ego vehicle with an ideal following distance of 40 m following a leader vehicle traveling at 25 m/s may use a similar braking leader critical constraint line as discussed above in FIG. 9I. Note that in FIG. 9J, the y-axis plots the actual speed of the ego vehicle as opposed to the relative speed of the ego vehicle.

As illustrated in FIG. 9K, a vehicle control system for an ego vehicle may utilize upper and lower LQR saturation boundaries. When a state of an ego vehicle rises above an upper LQR saturation boundary or below a lower LQR saturation boundary, the vehicle control system may implement LQR saturation in its control of the ego vehicle.

As illustrated in FIG. 9L, an ego vehicle with an ideal following distance of 40 m following a leader vehicle traveling at 25 m/s may use similar upper and lower LQR saturation boundaries as discussed above with FIG. 9K. Note that in FIG. 9L, the y-axis plots the actual speed of the ego vehicle as opposed to the relative speed of the ego vehicle.

Using the constraints and control plans as discussed herein, a vehicle may follow an overall contour as illustrated in FIG. 10A. As illustrated in FIG. 10A, the relative speed and relative distance of the ego vehicle compared with the leader vehicle may be plotted on y- and x-axes respectively. In the scenario illustrated in FIG. 10A, the speed of the leader vehicle is considered to be zero as when the ego vehicle is traveling at the same speed the ego's speed is plotted on the x-axis and considered to be also at zero, or equilibrium. The ideal following distance for the ego vehicle is set to be 8.6 meters, but this should be considered as being for example purposes only. As illustrated in the plot of FIG. 1A, as the ego vehicle travels at a high relative speed and becomes closer and closer to the leader vehicle, the contour plot of the ego vehicle becomes more and more extreme. A vertical line illustrates a maximum deceleration of the ego vehicle.

As illustrated in FIG. 10B, a contour plot of total control of the ego vehicle looks drastically different when the y-axis plots the actual speed of ego vehicle. In the contour plot of FIG. 10B, the leader vehicle is traveling at a constant speed of 25 m/s and the ideal following distance is set at 38.6 meters.

Speed Profile Generation

Utilizing systems described above and herein, a longitudinal planning system of an ego vehicle may be enabled to execute a longitudinal planner for the ego vehicle. The longitudinal planner may effectively be an artificial intelligence system capable of generating a speed profile for the vehicle. The speed profile may be in the form of instructions executable by the longitudinal planning system. Using a speed profile generated using a longitudinal planner as described herein, the longitudinal planning system may be capable of automatically managing the speed of the vehicle in order to keep the vehicle at a proper distance from a leader vehicle. In the event that no leader vehicle is in front of the ego vehicle, the ego vehicle may follow a particular set of instructions such as following a speed limit, driving at a safe speed given current weather and road conditions, etc. The longitudinal planner may be capable of ensuring avoidance of collision with a leading vehicle given certain assumptions; tracking a specified amount of headway time between the vehicle and the leader vehicle; obeying specified speed limits; following feedforward rules (e.g., from a lane change procedure system) if safe; applying limited acceleration and deceleration; and/or maintaining passenger comfort.

In some embodiments, a longitudinal planning system of an ego vehicle may use as inputs a distance between the ego vehicle and the leader vehicle, a measured or estimated velocity of the leader vehicle, and the measured or estimated velocity of the ego vehicle. Such inputs may be measured by systems of the ego vehicle. For example, the distance between the ego vehicle and the leader vehicle may be measured by a radar system or other system.

The longitudinal planning system may output a control plan for the ego vehicle. For example, the control plan may be an acceleration to be carried out by of the ego vehicle. In some embodiments, the longitudinal planning system may assume that the possible acceleration of the ego vehicle is bounded but can change instantaneously (i.e. no jerk constraints). The longitudinal planning system may also assume the leader vehicle has a bounded deceleration and will never drive backwards.

In some embodiments, a goal of the longitudinal planner may be to determine and designate an ideal or proper following distance (d*) behind a nearest leading vehicle. For example, the d* may be a distance (e.g. meters) such that if the ego vehicle is following at a distance of d* behind the leader vehicle the ego vehicle can be assuredly capable of coming to a complete and safe stop even in the extreme circumstance that the leader vehicle immediately brakes to its maximum capability.

The proper following distance (d*) may correspond to a target following time, t_(follow). The t_(follow) may represent the amount of time (e.g. seconds) that it would take the ego vehicle to reach the baseline distance behind the leader vehicle if the leader vehicle were to instantaneously come to a stop in front of the ego vehicle and the ego vehicle continued at its current speed none-the-less. In some embodiments, the proper following distance, d*, may be related to the target following time, t_(follow), by a formula such as: d*=d₀+v_(e)×t_(follow), where d₀ is a baseline distance (e.g. in meters) reflecting a safe distance between the ego vehicle and the leader vehicle when the vehicles are not moving. For example, if the leader vehicle stops the ego vehicle should stop behind it at a distance equal to d₀. The symbol v_(e) represents a current velocity of the ego vehicle.

Assuming that the leader vehicle velocity, v_(l), remains constant (making the acceleration of the leader vehicle, a_(leader), equal to zero), the error dynamics of the system may be represented as follows:

${{\frac{d}{dt}\begin{bmatrix} {d - d^{*}} \\ {v_{e} - v_{l}} \end{bmatrix}} = {{\begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix}\begin{bmatrix} {d - d^{*}} \\ {v_{e} - v_{l}} \end{bmatrix}} + {\begin{bmatrix} {- t_{follow}} \\ 1 \end{bmatrix}u}}},$

where u represents the target acceleration of the ego vehicle. In some embodiments, u, may be an output of the longitudinal planner to a longitudinal planning system in order to control the vehicle at the proper distance behind the leader vehicle. The longitudinal planning system may use the target acceleration to control the vehicle and keep the vehicle at a proper speed given a state of the leader vehicle.

Given the dynamics of the matrices

${\begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix}{\mspace{11mu} \;}{{and}{\; \mspace{11mu}}\begin{bmatrix} {- t_{follow}} \\ 1 \end{bmatrix}}},$

in the above error dynamics formula, cost matrices Q and R which respectively quadratically penalize a distance error (d-d*) and a speed difference (v_(e)-v_(l)) may be defined. Next, an optimal linear feedback control, u=−K[d-d*; v_(e)-v_(l)], may be solved for in the absence of any constraints. Near the origin, i.e. when the ego and leader vehicle are travelling at or near the same speed and when the ego and leader vehicle are near in proximity, this feedback control may provide a smooth and continuous convergence to an equilibrium point.

Linear-quadratic regulator (LQR) control systems are naturally optimal, can achieve good phase margin, and are easy to tune in terms of reducing overshoot or oscillations. However, two primary short-comings of a LQR based longitudinal planning system are that such a system does not anticipate any acceleration from a leader vehicle (e.g. example using critical constraints) and the system does not account for input saturations (e.g. using critical and/or other constraints). Otherwise, when the error is small and the saturation constraints are inactive, an LQR control system can be an optimal solution for smoothly tracking the origin. For this reason, what is needed is a dynamic longitudinal planning system capable of overcoming such short-comings. As described herein, a longitudinal planning system may both account for leader acceleration and account for input saturations using constraints.

Collision Avoidance

In some embodiments, the longitudinal planning system may assume that at any point in time the leader vehicle may instantaneously and/or spontaneously initiate braking at a maximum deceleration to a complete stop. The longitudinal planning system may also assume that the leader vehicle has some maximum deceleration (a_(crit)), where a_(crit) is less than zero (i.e., deceleration). The longitudinal planning system may assume that while the leader vehicle is braking, the magnitude of the deceleration of the leader vehicle will not exceed its a_(crit) (for example negative 5 m/s²) i.e., will not decelerate at a greater rate than the assumed maximum deceleration. The longitudinal planning system may also assume that it can brake up to its own a_(crit) limit. A critical following distance (d_(crit)) may be determined as a point which is the minimum following distance for which the ego vehicle of the ego and leader vehicles may safely stop behind the leader vehicle given the current velocity and the maximum deceleration for the leader and ego vehicles. The longitudinal planning system may constantly attempt to enforce acceleration/deceleration as may be determined by the longitudinal planning system dynamically. For example, the longitudinal planning system may seek to guarantee its current following distance (d) is greater than or equal to d_(crit) at all times.

By algebraically solving kinematics for a constant acceleration motion profile, it can be shown that, given a velocity of an ego vehicle (v_(e)), a leader speed (v_(l)) that is less than v_(e), and a current distance margin (with respect to the critical value) of d-d_(crit), and if it is assumed that both vehicles apply some constant deceleration a_(required) given by

${a_{required} = \frac{v_{l}^{2} - v_{e}^{2}}{2\left( {d - d_{crit}} \right)}},$

the leader vehicle will come to a stop first (at which point the acceleration of the leader vehicle (a_(leader)) will change from a_(required) to zero), and the ego car will then later come to a stop at exactly d=d_(crit).

Derivation Outline

If both the ego and leader vehicles apply the same deceleration, a_(required), for a first amount of time (Δt_(l)=v_(l)/−a_(required) (the time it takes the leader vehicle to stop)), the relative velocity of the ego and leader vehicles, v_(e)-v_(l) should not change. During this time, the distance between the ego and leader vehicles changes by (v_(e)-v_(l))Δt_(l)=(v_(e)-v_(l))v_(l)/−a_(required).

After the first amount of time, the leader vehicle stops and stays at rest while the ego vehicle continues to brake. A second amount of time (Δt₂=(v_(e)-v_(l))/−a_(required)) passes before the ego vehicle comes to a stop. Over this second amount of time, the distance between the vehicles, d, changes by

${{\left( {v_{e} - v_{l}} \right)\Delta \; t_{2}} - {\frac{- a_{required}}{2}\Delta \; t_{2}^{2}}} = {\frac{\left( {v_{e} - v_{l}} \right)^{2}}{{- 2}a_{required}}.}$

Adding the distance changes together across both the first and second amounts of time and yields the equation,

${a_{required} = \frac{v_{l}^{2} - v_{e}^{2}}{2\left( {d - d_{crit}} \right)}},$

discussed above.

It can be appreciated that a_(required) (as a function of the current state of the ego and leader vehicles [v_(e); v_(l); d]) increases (approaches 0) whenever a_(ego)<a_(leader) and decreases (approaches −∞) whenever a_(ego)>a_(leader). Furthermore, by applying a control acceleration, u, equal to a_(crit) as soon as a_(required)=a_(crit), the longitudinal planning system can guarantee that, at least in a theoretical sense, the ego vehicle will react sufficiently to stop with over the distance d_(crit) even in the worst-case scenario (where the leader vehicle also applies a_(crit) until the leader vehicle stops). Due to ego and leader state measurement and prediction errors, control inaccuracy, unmodeled dynamics, software latency, etc., the theoretical safe following distance may be insufficient to properly ensure collision avoidance. For this reason, the following distance may be increased to account for such variables. Note that this control law, u=a_(ego)=a_(crit) if a_(required)=a_(crit), simultaneously guarantees that a_(required) never exceeds a_(crit).

In some embodiments, it may be preferable to not implement an uncomfortable control law that switches discontinuously to this behavior of u=a_(crit) only when a_(required)=a_(crit). Instead, a blending factor that lets u approach a_(required) as a_(required) approaches a_(crit) may be applied by the longitudinal planning system. Such a blending factor may achieve a more conservative and smoother behavior.

Constraints for Input Saturation

In some embodiments, the longitudinal planning system of an ego vehicle may execute a longitudinal planner which may attempt to control the ego vehicle to follow a desired distance behind a leader vehicle given the velocity of the leader vehicle's speed and to maintain the avoidance of the possibility of a collision with the leader vehicle. Such a longitudinal planner should balance both safety and comfort factors. Also, since the possible acceleration and deceleration limits of each of the leader and ego vehicles must be a finite amount and cannot be infinitely large, constraints should be placed on the possible acceleration and deceleration.

The longitudinal planning system of the ego vehicle may set a critical distance for the extreme case where the leader vehicle brakes at its maximum deceleration limit and where the leader vehicle continues to brake at its maximum deceleration limit until it stops. The critical distance may be a distance such that the ego vehicle will, in the extreme case, end up at a proper distance from the leader vehicle once the leader and ego vehicles are stopped. This is the critical constraint such that any state of the ego and leader vehicles should require maximum deceleration of the ego vehicle.

The longitudinal planning system must also be capable of dealing with two other general scenarios: one where the ego vehicle is at a distance from the leader vehicle that is greater than an ideal following distance; and the other where the ego vehicle is too close to the leader vehicle given the ideal following distance.

In the scenario where the ego vehicle is further than an ideal distance from the leader vehicle and tries to catch up with the leader vehicle from some distance greater than the ideal following distance, the longitudinal planning system may control the ego vehicle and try to converge with a mild acceleration to a distance range that is further than or equal to the desired following distance, d*, for the steady state (i.e., vehicles become equalized on speed), any conditions which violate this need to apply an upper acceleration limit (or maximum acceleration) to catch up the leader vehicle. Since this distance in steady state may be greater than the desired following distance, input constraints associated with this case may be defined as overshoot constraints.

Similarly, in the other scenario where the ego vehicle is too close to the leader vehicle, the longitudinal planning system of the ego vehicle should attempt to brake to develop gap for the distance to converge to a range that closer than the desired following distance with a mild deceleration. In the event that the ego vehicle is overly close to the leader vehicle, the longitudinal planning system should require the ego vehicle to brake at a deceleration limit for this case. Input constraints associated with this case may be defined as undershoot constraints.

Without loss of generality, in classical control theory, when an actual signal is greater than a reference value, it is called overshoot which means the actual signal has passed the reference value, and vice versa. So, by checking if the current distance, d, of the ego vehicle from the leader vehicle is greater or smaller than the ideal following distance, d*, the condition may be described as overshoot or undershoot, respectively. However, in a longitudinal control scenario, when the ego vehicle is approaching to the leader by acceleration, the distance actually decreases. Once the distance passes the desired distance, i.e., d<d*, the vehicle may be described as having overshot the desired distance.

As used herein, the acceleration control input, u, is the acceleration/deceleration of the ego vehicle. The symbol u⁺ may be defined as accelerations while the symbol u⁻ may be defined as deceleration, where u⁻<0<u⁺.

Overshoot Constraint

Overshoot constraint may be implemented in the scenario where the ego vehicle is at a distance greater than the ideal following distance from the leader vehicle and is narrowing the distance from the leader vehicle by traveling with a moderate acceleration. The ego vehicle may be approaching the leader vehicle from a far distance. The ego vehicle may be travelling with a mild acceleration and its acceleration input may be saturating at a maximum limit. This maximum acceleration limit, a_(max) ⁺, may be considered to be a mild acceleration limit, for example between 1 and 1.5 m/s². As used herein, a superscript plus sign can be interpreted as meaning acceleration while deceleration which can be denoted using a superscript minus sign.

In such a scenario, the longitudinal planning system may consider only the saturation factors, which may be the overshoot constraint for the acceleration (as opposed to considering any deceleration constraint). For this reason, the longitudinal planning system may need to consider only the maximum allowed acceleration, u⁺, and not any minimum allowed deceleration, u⁻. A positive real number d_(o)* that is greater than d* may be defined as a desired distance for the steady state of the overshoot case, wherein steady state indicates that the longitudinal planning system may focus on the state that the ego vehicle's speed becomes equalized with the leader vehicle speed. In some embodiments, but not all, the desired distan1ce for the steady state of the overshoot case, d_(o)*, may be set to a greater amount than the desired following distance d* for the steady state. Next, a positive real number, d^(b), that is greater than zero may be defined as a threshold for blending the overshoot constraint.

As illustrated in FIG. 9C, the general instructions for an ego vehicle following a leader vehicle may be plotted on a phase portrait. The vertical axis of the phase portrait illustrates the relative speed of the ego vehicle compared to the leader vehicle. For example, points above the speed of 0 m/s reflect states in which the ego vehicle is faster than the leader vehicle and points below the speed of 0 m/s reflect states in which the ego vehicle is slower than the leader vehicle. The horizontal axis illustrates the relative position of the ego vehicle compared to the leader vehicle. For example, points toward the left side of the horizontal axis reflect states in which the ego vehicle is near the leader vehicle while points toward the right side of the horizontal axis reflect states in which the ego vehicle is relatively further from the leader vehicle.

In the example phase portrait illustrated in FIG. 9C, an ideal following distance behind the leader vehicle may be set at 8.6 meters. For example, the ego vehicle may ideally be travelling at the ideal following distance at the same speed as the leader vehicle.

The curved lines on the phase portrait may represent motion plans to be followed by the ego vehicle. For example, motion plans for states in the upper right-hand portion of the phase portrait may require the ego vehicle to move closer to the leader and also to decrease its relative speed. Similarly, points in the lower righthand portion may require the ego vehicle to move closer to the leader vehicle while increasing the relative speed. These motion plan lines represent theoretically ideal motion plans. However, these lines do not take into consideration factors such as maximum and/or minimum acceleration given comfort and/or vehicle-specific requirements. For example, where the motion plans move in a straight vertical line, the motion plan falls into an impossible discontinuity either calling for an unrealistically high or low acceleration. For this reason, as discussed below, a blending zone may be created specifying particular rules for the ego vehicle to follow when near these discontinuities.

Because of these discontinuities, the overshoot scenario may be split into three scenarios are considered for the overshoot constraint: (1) standard case, (2) violation case, and (3) blending case.

In the standard case, the actual following distance of the ego vehicle is smaller than the desired overshoot distance by d^(b), i.e., d-d_(o)*<d^(b). Two cases are considered here:

One, where the ego vehicle is slower than the leader vehicle, i.e., v_(e)<v_(l). In this scenario, there is still some room to apply non-zero acceleration. In such a scenario, the ego vehicle may set the goal acceleration according to the following formula:

$u^{+} = {{\max \left\lbrack {0,{\min \left\lbrack {\frac{\left( {v_{e} - {v.}} \right)^{2}}{{- 2}\left( {d - d_{O}^{*}} \right)},a_{\max}^{+}} \right\rbrack}} \right\rbrack}.}$

By applying this acceleration and given the situation that the ego vehicle is slower, the longitudinal planning system may guarantee that the actual distance will converge to d_(o)* as the ego speed, v_(e), approaches v_(l).

Two, where the ego vehicle is faster than or the same speed as the leader vehicle, i.e., v_(e)≥v_(l). Since the actual distance is less than the desired overshoot distance and the ego vehicle travels faster than the leader vehicle. There is no need to have accelerations, i.e., throttle needs to be zero if the ego vehicle has throttle/brake actuation (vs. torque). As a result, the goal acceleration may be set to zero.

In the violation case, the actual following distance is greater than the desired overshoot distance, i.e. d≥d_(o)*. This means that the overshoot constraint is violated (in the sense of an optimization problem formulation). For example, as illustrated in FIG. 9G, the horizontal portion of the dashed overshoot trajectory on the equal speed line (0 on the y-axis)) indicates points where the ego vehicle is in the violation case and travelling at the same speed as the leader vehicle. Points near but above the horizontal portion of the dashed overshoot trajectory represent states where the ego vehicle is in the violation case travelling faster than the ego vehicle and points near but below the horizontal portion of the dashed overshoot trajectory represent states where the ego vehicle is in the violation case travelling slower than the ego vehicle. Depending whether the ego vehicle is slower or faster than the leader vehicle, the ego vehicle can choose to accelerate at the mild acceleration limit (slower than the leader) or coast without applying an acceleration (faster than the leader). This causes a discontinuity of the acceleration. This discontinuity is created by a motion plan of a vertical line in states where the ego vehicle is further from the leader vehicle than an idea position. These discontinuity states occur at the points where the ego and leader vehicles are travelling at the same speed (on the 0 m/s line of the y-axis). Above the 0 m/s line, the ego vehicle is faster than the leader vehicle, so the ego vehicle can coast with no applied acceleration. Below the 0 m/s line, the ego vehicle is slower than the leader vehicle, so the ego vehicle needs to apply a mild acceleration limit. This corresponds to the violation case. Hence, to remove this discontinuity, a blending region is created to blend the acceleration.

In a violation above case, the ego vehicle is faster than the leader vehicle by v^(b), i.e., v_(e)-v_(l)≥v^(b), where v^(b) defines a positive real number as the threshold for blending the overshoot constraint. Since the speed error is above the blending threshold, the actual will converge to the desired overshoot automatically without requiring any acceleration. As a result, the target acceleration may be set to zero.

In a violation below case, the ego vehicle is slower than the leader vehicle. For this case, both the actual distance is far away, and the ego speed is slower than the leader vehicle. Hence, the ego vehicle needs to use maximum allowed acceleration to accelerate and the target acceleration may be set to a_(max) ⁺.

In a violation blend case, the longitudinal planning system may create a blending region for when the velocity of the ego vehicle exceeds the velocity of the leader vehicle by a predetermined blending amount, v^(b), (e.g. 5 m/s). When the ego speed reaches and/or exceeds the leader speed to enter this region, the acceleration input may be blended in this region by a linear interpolation using a formula such as

$u^{+} = {{a_{\max}^{+}\left( {1 - \frac{v_{e} - v_{l}}{v^{b}}} \right)}.}$

Since the ego speed is in the region described above, 0≤v_(e)-v_(l)<v^(b), the acceleration will gradually go from the mild acceleration limit a_(max) ⁺ to 0 and vice versa.

In a blending case, the actual following distance of the ego vehicle is within a desired overshoot distance but does not exceed a threshold for the blending, i.e., d_(O)*-d^(b)≤d<d_(O)*. When the distance between the ego vehicle and the leader vehicle crosses the overshoot constraint line, the gradient of the target acceleration is approaching infinity which causes another discontinuity. For example, on the right-hand side region of the phase portrait illustrated in FIG. 9C, it is usually the case that the ego vehicle is too far from the leader vehicle and travelling at a less than optimal speed. In such a scenario, a_(max) ⁺ is applied. As a further example, on the left-hand side region of the phase portrait illustrated in FIG. 9C, it is usually the case that the ego vehicle is too closer to the leader vehicle and depending on whether the ego vehicle is faster or slower than the leader vehicle, the target acceleration can be small or zero. For this reason, the longitudinal planning system may create a blending region on the left-hand side with a width of d^(b) for the blending.

The longitudinal planning system may follow a particular method to calculate the target acceleration in this region. For the violation case, the zero acceleration speed difference and maximum acceleration speed difference are defined at v^(b) and zero, respectively, i.e., when the speed difference is at v^(b) the ego vehicle acceleration u⁺=zero and when the speed difference is at 0 the ego vehicle acceleration u⁺=a_(max) ⁺. However, since the blending case considers the region on the left-hand side of the overshoot constraint trajectory, the zero-acceleration speed and maximum acceleration speed need to shift down based on the distance difference. Define v_(min) ^(b)=−√{square root over ((d−d_(O)*)a_(max) ⁺)} the speed difference for the ego vehicle at the distance difference db with accelerates a_(max) ⁺. Then, the ego speed and leader speed may be equalized when the distance difference is 0. Next, the longitudinal planning system may define:

$d_{frac} = {- \frac{d - d_{O}^{*}}{d^{b}}}$

as a distance difference fraction; v_(a0)=(1-d_(frac))v^(b) as a zero-acceleration speed; v_(amax)=d_(frac)v^(b) as a max acceleration speed;

$v_{frac} = \frac{v_{e} - v_{l} - v_{a\; 0}}{v_{a\; \max} - v_{a\; 0}}$

as a speed difference fraction;

$a_{left} = \frac{\left( {v_{frac}v_{\min}^{b}} \right)^{2}}{2*d^{b}}$

as an acceleration for the left edge of the blending region; and a_(right)=v_(frac)a_(max) ⁺ as an acceleration for the right edge of the blending region. As a result, the blended acceleration may be calculated as u⁺=(1−d_(frac))a_(right)+d_(frac)a_(left).

Undershoot Constraint

Undershoot constraint deals with a scenario where the ego vehicle is driving near a leader vehicle and when there is still room for the ego vehicle to use a deceleration within a mild deceleration limit, for example −1 m/s², to develop a desired gap from the leader vehicle to reach the critical distance. Such a deceleration limit may be defined as a_(eq) ⁻. When applying undershoot constraints, the longitudinal planning system may consider only constraint for the deceleration (as opposed to considering acceleration constraints), that is, it only deals with u⁻.

As illustrated in FIG. 9G, the curved portion of the solid undershoot constraint line represents states where the target acceleration is at or less than the deceleration limit a_(eq) ⁻. There are three scenarios to be considered for the undershoot constraint scenario: a standard case, where the actual following distance is greater than a desired undershoot distance by a blending distance, d^(b); a violation case, where the actual following distance is less than a desired undershoot distance d_(u)*; and a blending case where the actual following distance is greater than a desired undershoot distance but does not exceed the threshold for the blending.

In the standard case, the actual following distance, d, of the ego vehicle is greater than the desired undershoot distance, d_(u)*, by d^(b), i.e., d-d_(u)*>d^(b). In the standard case, two cases are considered: one, where the ego vehicle is faster than the leader vehicle; and two, where the ego vehicle is slower than the leader vehicle.

In the first of the standard cases, the ego vehicle is faster than the leader vehicle, i.e., v_(e)>v_(l). In this scenario, there is still some room to apply non-zero deceleration. The longitudinal planning system should, in this case, apply a relatively maximum deceleration

${{as}\text{:}\mspace{11mu} u^{-}} = {{\max \left\lbrack {a_{eq}^{-},{\min \left\lbrack {\frac{- \left( {v_{e} - v_{l}} \right)^{2}}{2\left( {d - d_{u}^{*}} \right)},0} \right\rbrack}} \right\rbrack}.}$

By applying this relatively maximum deceleration and given the situation that the ego vehicle is faster, the longitudinal planning system may guarantee that the actual distance between the ego and leader vehicles will converge to d_(u)* as the ego speed v_(e)→v_(l).

In the second of the standard cases, the ego vehicle is slower than the leader vehicle, i.e., v_(e)≤v_(l). Since the actual distance between the ego and leader vehicles is greater than the desired undershoot distance and the ego vehicle travels slower than the leader vehicle, there is no need to have deceleration, i.e., the braking factor of the ego vehicle should be zero if the ego vehicle has throttle/brake actuation (as opposed to torque). In this case, the acceleration of the ego vehicle, u⁺, should be set to zero.

In the violation case, the actual following distance of the ego vehicle is less than the desired undershoot distance, i.e. d≤d_(u)*. This means that the undershoot constraint is violated (in the sense of an optimization problem formulation). When the speed of the ego vehicle compared to the speed of the leader vehicle crosses the 0 m/s relative speed line in the vertical direction, the ego speed goes from slower to faster than the leader speed, or vice versa. Depending whether the ego vehicle is slower or faster than the leader vehicle, the ego vehicle can choose to decelerate at an equalized deceleration limit, a_(eq) ⁻, (when the ego vehicle is faster than the leader vehicle) or coast with zero deceleration (when the ego vehicle is slower than the leader vehicle). This causes a discontinuity of the deceleration. Hence, a blending region is created to blend the deceleration to remove the discontinuity. There are three scenarios to consider in the violation case: where the ego vehicle is faster than the leader vehicle (violation above case), where the ego vehicle is slower than the leader vehicle by at least a blending velocity factor (violation below case), and where the ego vehicle is slower than the leader vehicle by less than the blending factor (violation blend case).

In the violation above case, the ego vehicle is faster than the leader vehicle, i.e., v_(e)-v_(l)>0. Since the speed error is above zero, the ego vehicle needs to use the maximum allowed deceleration, that is a_(eq) ⁻ to deceleration. As such, the longitudinal planning system may set the target deceleration to a_(eq) ⁻.

In the violation below case, the ego vehicle is slower than the leader vehicle by v^(b), i.e., v_(e)-v_(l)≤−v^(b). For this case, the ego vehicle does not need to apply deceleration and the longitudinal planning system may set the target deceleration to zero.

In the violation blend case, where the ego vehicle is slower than the leader vehicle by less than the blending factor, the longitudinal planning system may create a blending region below the horizontal portion of the dashed overshoot constraint line, such as that illustrated in FIG. 9G, by v^(b). When the ego speed crosses the leader speed line to enter this region, the deceleration input is blended in this region by a linear interpolation. For example, the deceleration input may be set as follows by the formula:

$u^{-} = {{a_{eq}^{-}\left( {1 + \frac{v_{e} - v_{l}}{v^{b}}} \right)}.}$

Since the ego speed is in the region described above, −v^(b)<v_(e)-v_(l)≤0. As a result, the deceleration will gradually go from the equalized deceleration limit a_(eq) ⁻ to 0 and vice versa.

In the blending case, the actual following distance of the ego vehicle behind the leader vehicle is greater than the desired undershoot distance but does not exceed a threshold distance, d^(b), for the blending, i.e., d_(u)*<d≤d^(b)+d_(u)*. When the distance between the ego vehicle and the leader vehicle crosses the solid undershoot constraint line near the 0 m/s relative speed line, the gradient of the target acceleration is approaching infinity which causes a discontinuity. For example, on the left-hand side region of the phase portrait as illustrated in FIG. 9C, it is usually the case that the ego vehicle is too close to the leader vehicle and travelling faster than ideal and as a result the equalize deceleration limit a_(eq) ⁻ is applied. On the right-hand side region of the phase portrait illustrated in FIG. 9C, it is usually the case that the ego vehicle does not violate the undershoot distance and, depending on whether the ego vehicle is faster or slower than the leader, the target deceleration can be small or zero. Hence, the longitudinal planner system may create a blending region on the right-hand side with a width of d^(b) for the blending. The method to calculate the deceleration in this region is similar to the overshoot case.

Critical Constraint

The application of critical constraint is similar to the application of undershoot constraint as discussed above, except that the longitudinal planning system may consider the possibility of the leader vehicle braking at the deceleration limit a_(crit) ⁻. The longitudinal planning system may deal with the case that if both the ego and leader vehicles decelerate at the same deceleration, the ego vehicle should come to a stop at a critical distance d_(crit) from the leader vehicle after the leader vehicle has stopped. This critical distance may be less than the undershoot distance and may be considered as the minimum safety boundary to guarantee collision avoidance. In the critical constraint scenario, there are three cases: a standard case, in which the actual following distance of the ego vehicle is greater than the desired critical distance by at least a blending threshold amount; a violation case, in which the actual following distance of the ego vehicle is less than the critical distance; and the blending case, in which the actual following distance of the ego vehicle is within the critical distance but does not exceed the threshold for the blending.

In the standard case, the actual following distance is greater than the desired critical distance by d^(b), i.e., d-d_(crit)>d^(b). Two cases are considered in the standard case: one, where the ego vehicle is faster than the leader vehicle and two, where the ego vehicle is not faster than the leader vehicle.

In the first case, where the ego vehicle is faster than the leader vehicle, i.e., v_(e)>v_(l), there is still some room to apply non-zero deceleration. The longitudinal planning system may set the deceleration of the ego vehicle according to the following formula:

$u^{-} = {{\max \left\lbrack {a_{crit}^{-},{\min \left\lbrack {\frac{v_{l}^{2} - v_{e}^{2}}{2\left( {d - d_{crit}} \right)},0} \right\rbrack}} \right\rbrack}.}$

By applying this deceleration and given the situation that the ego vehicle is faster than the leader vehicle, the ego vehicle may guarantee that the actual distance will converge to the proper overshoot distance, d_(O)*, as the ego vehicle speed, v_(e), approaches the speed of the leader vehicle, v_(l).

In the second case, where the ego vehicle is slower than or the same speed as the leader vehicle, i.e., v_(e)≤v_(l); Since the actual distance is greater than the critical distance, and the ego vehicle travels slower than the leader vehicle, there is no need to have applied deceleration, i.e., the brake can be zero if the ego vehicle has throttle/brake actuation (vs. torque). As such, the target acceleration of the ego vehicle, u⁺, may be set to zero.

In the violation case, the actual following distance of the ego vehicle is less than or equal to the critical distance, i.e. d≤d_(crit). This means that the critical constraint is violated. When the actual speed of the ego vehicle crosses the horizontal axis in the vertical direction, the ego speed goes from slower to faster than the leader speed, or vice versa. Depending on whether the ego vehicle is slower or faster than the leader vehicle, the ego vehicle can choose to decelerate at the critical deceleration limit (faster than the leader) or coast with zero applied deceleration (slower than the leader). This causes a discontinuity of the deceleration. For this reason, a blending region may be created by the longitudinal planning system to blend the deceleration to remove the discontinuity.

In the violation above case, the ego vehicle is faster than the leader vehicle, i.e., v_(e)>v_(l)>0. Since the speed error is above zero, the ego vehicle needs to set its deceleration, u⁻, to the maximum allowed deceleration, a_(crit) ⁻, to decelerate.

In the violation below case, the ego vehicle is slower than the leader vehicle by at least a critical blend case velocity, v^(b), i.e., v_(e)-v_(l)←−v_(crit). For this case, the ego vehicle does not need to apply deceleration and the acceleration of the ego vehicle may be set to zero.

In the violation blend case, the ego vehicle may create a blending region by v^(b). When the ego speed crosses the leader speed line to enter this region, the deceleration input is blended in this region by a linear interpolation. The longitudinal planning system may in such a scenario set the ego vehicle's deceleration according to the following formula:

$u^{-} = {{a_{crit}^{-}\left( {1 + \frac{v_{e} - v_{l}}{v^{b}}} \right)}.}$

As a result, when the ego speed is in the region described above, −v^(b)<v_(e)-v₁≤0, the deceleration will gradually go from the equalized deceleration limit a_(eq) ⁻ to 0 and vice versa.

In the blending case, the actual following distance of the ego vehicle is within the critical distance but does not exceed the threshold for the blending, i.e., d_(crit)<d≤d^(b)+d_(crit). When the distance crosses the critical constraint line near the 0 m/s relative speed line, the gradient of the target acceleration is approaching infinity which causes another discontinuity. For instance, on the left-hand side region of the phase portrait as illustrated in FIG. 9C, it is usually the case that the ego vehicle is too close to the leader vehicle and travelling faster than ideal so that the deceleration a_(crit) ⁻ is applied. On the right-hand side region, it is usually the case that the ego vehicle does not violate the undershoot distance and, depending on whether the ego vehicle is faster or slower than ideal, the target deceleration can be small or zero. Hence, the longitudinal planner system may create a region on the right-hand side of the phase portrait as illustrated in FIG. 9C with a width of d^(b) for the blending. The method to calculate the deceleration in this region is similar to the overshoot and undershoot cases except that it considers both vehicles braking at the deceleration limit and the derivation will be skipped.

Blending of the Constraints

Using LQR systems, the longitudinal planning system may be capable of blending overshoot control as discussed above. The longitudinal planning system may set the upper and lower acceleration/deceleration boundaries to blend the overshoot control with LQR. When the overshoot control is at an upper boundary, the longitudinal planning system may utilize full overshoot control. When the overshoot control is below a lower boundary, the longitudinal planning system may utilize full LQR control. The longitudinal planning system may blend its control scheme between these two. After blending the overshoot with LQR control, the longitudinal planning system may blend the undershoot control in the same way. Similarly, after blending the overshoot and undershoot controls, the longitudinal planning system may blend the control scheme with critical control. As a result, the longitudinal planning system may be capable of picking a minimum of the blending results and using the minimum as a control for distance keeping.

Speed Limit

In some embodiments, the longitudinal planning system may utilize a proportional control based on an error between a speed limit and the speed of the ego vehicle. Then, the acceleration/deceleration input may be saturated within a predefined minimum road speed acceleration and a maximum allowed acceleration. After calculating the control necessary to track the speed limit, the longitudinal planning system may determine a minimum of the speed limit control and the distance keeping control.

As illustrated in FIG. 11, a method may be performed by a longitudinal planning system. In some embodiments, a longitudinal planning system may be a vehicle control system. A longitudinal planning system may comprise a computing system. A longitudinal planning system may comprise a processor and memory. A processor of a longitudinal planning system may perform steps of the method illustrated in FIG. 11 or steps of other methods.

The method illustrated in FIG. 11 is used as an illustration of a possible embodiment of the present disclosure and should not be considered to be limiting in any way.

In some embodiments, a method may begin upon the occurrence of one of a number of events at step 1103. For example, the method may begin upon the vehicle starting, the vehicle beginning to move, the vehicle detecting the presence of a leader vehicle while travelling, a user such as a driver of the vehicle initiating the method, or other events. The method may be continuously running while the vehicle is powered on or may start and stop as needed.

After the method has initiated, the method may comprise the longitudinal planning system determining a state of the ego vehicle in step 1103. Determining the state of the ego vehicle may comprise reading measurements of sensors on or around the vehicle. Such sensors may be actively monitored, or the measurements may be read from memory accessible by the longitudinal planning system. The state of the ego vehicle may comprise information such as current velocity, current acceleration, acceleration capabilities of the vehicle, or other relative information. In some embodiments, the state of the ego vehicle may comprise information such as road conditions, speed limits, presence of other vehicles, or other information.

After, or contemporaneous to, determining the state of the ego vehicle in step 1103, the longitudinal planning system may next determine a state of a leader vehicle in step 1109. Determining the state of the leader vehicle may comprise identifying the leader vehicle. Certain aspects or qualities of the leader vehicle may be determined, such as current velocity of the leader vehicle, current acceleration of the leader vehicle, acceleration capabilities of the leader vehicle, or other information related to the leader vehicle.

Next, the longitudinal planning system may determine a critical distance based on the state of the ego vehicle and the state of the leader vehicle in step 1112. The critical distance may be determined based on a number of factors and may be the distance at which the ego vehicle can safely stop behind the leader vehicle in the scenario at which the leader vehicle immediately begins braking to its maximum capabilities. The critical distance may also be determined based on factors such as road conditions, leader vehicle type, etc.

Next, the longitudinal planning system may compare the distance between the ego vehicle and the leader vehicle with the critical distance in step 1115. In step 1115 the ego vehicle may also consider the speed of the ego vehicle and the speed of the leader vehicle and using the speeds of the vehicles and the distance between the vehicles, the longitudinal planning system may determine a state of the ego vehicle.

Based on the state of the ego vehicle and the state of the leader vehicle and the determined critical distance, the longitudinal planning system may apply one or more constraints in step 1118. These constraints may be as described herein and may affect the overall control plan of the ego vehicle.

Based on the state of the ego and leader vehicles and based on the application of the constraints, the longitudinal planning system may next determine a target acceleration and/or motion plan 1121 for the ego vehicle to follow. In step 1124, the longitudinal planning system of the ego vehicle may then control the ego vehicle to track the target acceleration and/or motion plan 1124. At step 1127, the method may end.

Any of the steps, functions, and operations discussed herein can be performed continuously and automatically.

The exemplary systems and methods of this disclosure have been described in relation to vehicle systems and electric vehicles. However, to avoid unnecessarily obscuring the present disclosure, the preceding description omits a number of known structures and devices. This omission is not to be construed as a limitation of the scope of the claimed disclosure. Specific details are set forth to provide an understanding of the present disclosure. It should, however, be appreciated that the present disclosure may be practiced in a variety of ways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show the various components of the system collocated, certain components of the system can be located remotely, at distant portions of a distributed network, such as a LAN and/or the Internet, or within a dedicated system. Thus, it should be appreciated, that the components of the system can be combined into one or more devices, such as a server, communication device, or collocated on a particular node of a distributed network, such as an analog and/or digital telecommunications network, a packet-switched network, or a circuit-switched network. It will be appreciated from the preceding description, and for reasons of computational efficiency, that the components of the system can be arranged at any location within a distributed network of components without affecting the operation of the system.

Furthermore, it should be appreciated that the various links connecting the elements can be wired or wireless links, or any combination thereof, or any other known or later developed element(s) that is capable of supplying and/or communicating data to and from the connected elements. These wired or wireless links can also be secure links and may be capable of communicating encrypted information. Transmission media used as links, for example, can be any suitable carrier for electrical signals, including coaxial cables, copper wire, and fiber optics, and may take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation to a particular sequence of events, it should be appreciated that changes, additions, and omissions to this sequence can occur without materially affecting the operation of the disclosed embodiments, configuration, and aspects.

A number of variations and modifications of the disclosure can be used. It would be possible to provide for some features of the disclosure without providing others.

In yet another embodiment, the systems and methods of this disclosure can be implemented in conjunction with a special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element(s), an ASIC or other integrated circuit, a digital signal processor, a hard-wired electronic or logic circuit such as discrete element circuit, a programmable logic device or gate array such as PLD, PLA, FPGA, PAL, special purpose computer, any comparable means, or the like. In general, any device(s) or means capable of implementing the methodology illustrated herein can be used to implement the various aspects of this disclosure. Exemplary hardware that can be used for the present disclosure includes computers, handheld devices, telephones (e.g., cellular, Internet enabled, digital, analog, hybrids, and others), and other hardware known in the art. Some of these devices include processors (e.g., a single or multiple microprocessors), memory, nonvolatile storage, input devices, and output devices. Furthermore, alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readily implemented in conjunction with software using object or object-oriented software development environments that provide portable source code that can be used on a variety of computer or workstation platforms. Alternatively, the disclosed system may be implemented partially or fully in hardware using standard logic circuits or VLSI design. Whether software or hardware is used to implement the systems in accordance with this disclosure is dependent on the speed and/or efficiency requirements of the system, the particular function, and the particular software or hardware systems or microprocessor or microcomputer systems being utilized.

In yet another embodiment, the disclosed methods may be partially implemented in software that can be stored on a storage medium, executed on programmed general-purpose computer with the cooperation of a controller and memory, a special purpose computer, a microprocessor, or the like. In these instances, the systems and methods of this disclosure can be implemented as a program embedded on a personal computer such as an applet, JAVA® or CGI script, as a resource residing on a server or computer workstation, as a routine embedded in a dedicated measurement system, system component, or the like. The system can also be implemented by physically incorporating the system and/or method into a software and/or hardware system.

Although the present disclosure describes components and functions implemented in the embodiments with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. Other similar standards and protocols not mentioned herein are in existence and are considered to be included in the present disclosure. Moreover, the standards and protocols mentioned herein, and other similar standards and protocols not mentioned herein are periodically superseded by faster or more effective equivalents having essentially the same functions. Such replacement standards and protocols having the same functions are considered equivalents included in the present disclosure.

The present disclosure, in various embodiments, configurations, and aspects, includes components, methods, processes, systems and/or apparatus substantially as depicted and described herein, including various embodiments, sub combinations, and subsets thereof. Those of skill in the art will understand how to make and use the systems and methods disclosed herein after understanding the present disclosure. The present disclosure, in various embodiments, configurations, and aspects, includes providing devices and processes in the absence of items not depicted and/or described herein or in various embodiments, configurations, or aspects hereof, including in the absence of such items as may have been used in previous devices or processes, e.g., for improving performance, achieving ease, and/or reducing cost of implementation.

The foregoing discussion of the disclosure has been presented for purposes of illustration and description. The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description for example, various features of the disclosure are grouped together in one or more embodiments, configurations, or aspects for the purpose of streamlining the disclosure. The features of the embodiments, configurations, or aspects of the disclosure may be combined in alternate embodiments, configurations, or aspects other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment, configuration, or aspect. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

Moreover, though the description of the disclosure has included description of one or more embodiments, configurations, or aspects and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights, which include alternative embodiments, configurations, or aspects to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges, or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges, or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

Embodiments include a method for controlling a vehicle, the method comprising: determining, by a processor of a longitudinal planning system of the vehicle, a state of the vehicle; determining, by the processor, a state of a leader vehicle; based on the determined state of the vehicle and the determined state of the leader vehicle, determining, by the processor, a critical distance for the vehicle; comparing, by the processor, a distance between the vehicle and the leader vehicle with the critical distance; based on the comparison, determining, by the processor, whether the vehicle is too close to or too far from the leader vehicle; based on the determination, applying, by the processor, one or more of overshoot constraints, undershoot constraints, and critical constraints; after applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, determining, by the processor, a target acceleration for the vehicle; and controlling, by the longitudinal planning system, the vehicle to track the target acceleration for the vehicle.

Aspects of the above method include wherein applying, by the processor, one or more of overshoot constraints, undershoot constraints, and critical constraints comprises applying each of overshoot constraints, undershoot constraints, and critical constraints.

Aspects of the above method include wherein applying each of overshoot constraints, undershoot constraints, and critical constraints comprises blending the overshoot constraints, undershoot constraints, and critical constraints.

Aspects of the above method include wherein overshoot constraints account for standard, violation, and blending cases.

Aspects of the above method include wherein undershoot constraints account for standard, violation, and blending cases.

Aspects of the above method include wherein determining the state of the vehicle comprises determining a velocity of the vehicle.

Aspects of the above method include wherein determining the state of the leader vehicle comprises determining a velocity of the leader vehicle and the distance between the vehicle and the leader vehicle.

Embodiments include a longitudinal planning system for controlling a vehicle, the longitudinal planning system comprising: a processor; and a computer-readable storage medium storing computer-readable instructions which, when executed by the processor, cause the processor to perform operations comprising: determining a state of the vehicle; determining a state of a leader vehicle; based on the determined state of the vehicle and the determined state of the leader vehicle, determining a critical distance for the vehicle; comparing a distance between the vehicle and the leader vehicle with the critical distance; based on the comparison, determining whether the vehicle is too close to or too far from the leader vehicle; based on the determination, applying one or more of overshoot constraints, undershoot constraints, and critical constraints; after applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, determining a target acceleration for the vehicle; and controlling the vehicle to track the target acceleration for the vehicle.

Aspects of the above the longitudinal planning system include wherein applying one or more of overshoot constraints, undershoot constraints, and critical constraints comprises applying each of overshoot constraints, undershoot constraints, and critical constraints.

Aspects of the above the longitudinal planning system include wherein applying each of overshoot constraints, undershoot constraints, and critical constraints comprises blending the overshoot constraints, undershoot constraints, and critical constraints.

Aspects of the above the longitudinal planning system include wherein overshoot constraints account for standard, violation, and blending cases.

Aspects of the above the longitudinal planning system include wherein undershoot constraints account for standard, violation, and blending cases.

Aspects of the above the longitudinal planning system include wherein determining the state of the vehicle comprises determining a velocity of the vehicle.

Aspects of the above the longitudinal planning system include wherein determining the state of the leader vehicle comprises determining a velocity of the leader vehicle and the distance between the vehicle and the leader vehicle.

Embodiments include a computer program product for controlling a vehicle, the computer program product comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code configured when executed by a processor of a longitudinal planning system of the vehicle to: determine a state of the vehicle; determine a state of a leader vehicle; based on the determined state of the vehicle and the determined state of the leader vehicle, determine a critical distance for the vehicle; compare a distance between the vehicle and the leader vehicle with the critical distance; based on the comparison, determine whether the vehicle is too close to or too far from the leader vehicle; based on the determination, apply one or more of overshoot constraints, undershoot constraints, and critical constraints; after applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, determine a target acceleration for the vehicle; and control the vehicle to track the target acceleration for the vehicle.

Aspects of the above the computer program product include wherein applying, by the processor, one or more of overshoot constraints, undershoot constraints, and critical constraints comprises applying each of overshoot constraints, undershoot constraints, and critical constraints.

Aspects of the above the computer program product include wherein applying each of overshoot constraints, undershoot constraints, and critical constraints comprises blending the overshoot constraints, undershoot constraints, and critical constraints.

Aspects of the above the computer program product include wherein overshoot constraints account for standard, violation, and blending cases.

Aspects of the above the computer program product include wherein undershoot constraints account for standard, violation, and blending cases.

Aspects of the above the computer program product include wherein determining the state of the leader vehicle comprises determining a velocity of the leader vehicle and the distance between the vehicle and the leader vehicle.

Any one or more of the aspects/embodiments as substantially disclosed herein.

Any one or more of the aspects/embodiments as substantially disclosed herein optionally in combination with any one or more other aspects/embodiments as substantially disclosed herein.

One or more means adapted to perform any one or more of the above aspects/embodiments as substantially disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C” “one or more of A, B or C” “A, B and/or C,” and “A, B, or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more,” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers to any process or operation, which is typically continuous or semi-continuous, done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodiment that is entirely hardware, an embodiment that is entirely software (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

The terms “determine,” “calculate,” “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique.

The term “electric vehicle” (EV), also referred to herein as an electric drive vehicle, may use one or more electric motors or traction motors for propulsion. An electric vehicle may be powered through a collector system by electricity from off-vehicle sources or may be self-contained with a battery or generator to convert fuel to electricity. An electric vehicle generally includes a rechargeable electricity storage system (RESS) (also called Full Electric Vehicles (FEV)). Power storage methods may include chemical energy stored on the vehicle in on-board batteries (e.g., battery electric vehicle or BEV), on board kinetic energy storage (e.g., flywheels), and/or static energy (e.g., by on-board double-layer capacitors). Batteries, electric double-layer capacitors, and flywheel energy storage may be forms of rechargeable on-board electrical storage.

The term “hybrid electric vehicle” refers to a vehicle that may combine a conventional (usually fossil fuel-powered) powertrain with some form of electric propulsion. Most hybrid electric vehicles combine a conventional internal combustion engine (ICE) propulsion system with an electric propulsion system (hybrid vehicle drivetrain). In parallel hybrids, the ICE and the electric motor are both connected to the mechanical transmission and can simultaneously transmit power to drive the wheels, usually through a conventional transmission. In series hybrids, only the electric motor drives the drivetrain, and a smaller ICE works as a generator to power the electric motor or to recharge the batteries. Power-split hybrids combine series and parallel characteristics. A full hybrid, sometimes also called a strong hybrid, is a vehicle that can run on just the engine, just the batteries, or a combination of both. A mid hybrid is a vehicle that cannot be driven solely on its electric motor, because the electric motor does not have enough power to propel the vehicle on its own.

The term “rechargeable electric vehicle” or “REV” refers to a vehicle with on board rechargeable energy storage, including electric vehicles and hybrid electric vehicles. 

What is claimed is:
 1. A method for controlling a vehicle, the method comprising: determining, by a processor of a longitudinal planning system of the vehicle, a state of the vehicle; determining, by the processor, a state of a leader vehicle; based on the determined state of the vehicle and the determined state of the leader vehicle, determining, by the processor, a critical distance for the vehicle; comparing, by the processor, a distance between the vehicle and the leader vehicle with the critical distance; based on the comparison, determining, by the processor, whether the vehicle is too close to or too far from the leader vehicle; based on the determination, applying, by the processor, one or more of overshoot constraints, undershoot constraints, and critical constraints; after applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, determining, by the processor, a target acceleration for the vehicle; and controlling, by the longitudinal planning system, the vehicle to track the target acceleration for the vehicle.
 2. The method of claim 1, wherein applying, by the processor, one or more of overshoot constraints, undershoot constraints, and critical constraints comprises applying each of overshoot constraints, undershoot constraints, and critical constraints.
 3. The method of claim 2, wherein applying each of overshoot constraints, undershoot constraints, and critical constraints comprises blending the overshoot constraints, undershoot constraints, and critical constraints.
 4. The method of claim 1, wherein overshoot constraints account for standard, violation, and blending cases.
 5. The method of claim 1, wherein undershoot constraints account for standard, violation, and blending cases.
 6. The method of claim 1, wherein determining the state of the vehicle comprises determining a velocity of the vehicle.
 7. The method of claim 1, wherein determining the state of the leader vehicle comprises determining a velocity of the leader vehicle and the distance between the vehicle and the leader vehicle.
 8. A longitudinal planning system for controlling a vehicle, the longitudinal planning system comprising: a processor; and a computer-readable storage medium storing computer-readable instructions which, when executed by the processor, cause the processor to perform operations comprising: determining a state of the vehicle; determining a state of a leader vehicle; based on the determined state of the vehicle and the determined state of the leader vehicle, determining a critical distance for the vehicle; comparing a distance between the vehicle and the leader vehicle with the critical distance; based on the comparison, determining whether the vehicle is too close to or too far from the leader vehicle; based on the determination, applying one or more of overshoot constraints, undershoot constraints, and critical constraints; after applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, determining a target acceleration for the vehicle; and controlling the vehicle to track the target acceleration for the vehicle.
 9. The longitudinal planning system of claim 8, wherein applying one or more of overshoot constraints, undershoot constraints, and critical constraints comprises applying each of overshoot constraints, undershoot constraints, and critical constraints.
 10. The longitudinal planning system of claim 9, wherein applying each of overshoot constraints, undershoot constraints, and critical constraints comprises blending the overshoot constraints, undershoot constraints, and critical constraints.
 11. The longitudinal planning system of claim 8, wherein overshoot constraints account for standard, violation, and blending cases.
 12. The longitudinal planning system of claim 8, wherein undershoot constraints account for standard, violation, and blending cases.
 13. The longitudinal planning system of claim 8, wherein determining the state of the vehicle comprises determining a velocity of the vehicle.
 14. The longitudinal planning system of claim 8, wherein determining the state of the leader vehicle comprises determining a velocity of the leader vehicle and the distance between the vehicle and the leader vehicle.
 15. A computer program product for controlling a vehicle, the computer program product comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code configured when executed by a processor of a longitudinal planning system of the vehicle to: determine a state of the vehicle; determine a state of a leader vehicle; based on the determined state of the vehicle and the determined state of the leader vehicle, determine a critical distance for the vehicle; compare a distance between the vehicle and the leader vehicle with the critical distance; based on the comparison, determine whether the vehicle is too close to or too far from the leader vehicle; based on the determination, apply one or more of overshoot constraints, undershoot constraints, and critical constraints; after applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, determine a target acceleration for the vehicle; and control the vehicle to track the target acceleration for the vehicle.
 16. The computer program product of claim 15, wherein applying, by the processor, one or more of overshoot constraints, undershoot constraints, and critical constraints comprises applying each of overshoot constraints, undershoot constraints, and critical constraints.
 17. The computer program product of claim 16, wherein applying each of overshoot constraints, undershoot constraints, and critical constraints comprises blending the overshoot constraints, undershoot constraints, and critical constraints.
 18. The computer program product of claim 15, wherein overshoot constraints account for standard, violation, and blending cases.
 19. The computer program product of claim 15, wherein undershoot constraints account for standard, violation, and blending cases.
 20. The computer program product of claim 15, wherein determining the state of the leader vehicle comprises determining a velocity of the leader vehicle and the distance between the vehicle and the leader vehicle. 